idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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2,801 | Performance metrics to evaluate unsupervised learning | The most voted answer is very helpful, I just want to add something here. Evaluation metrics for unsupervised learning algorithms by Palacio-Niño & Berzal (2019) gives an overview of some common metrics for evaluating unsupervised learning tasks. Both internal and external validation methods (w/o ground truth labels) a... | Performance metrics to evaluate unsupervised learning | The most voted answer is very helpful, I just want to add something here. Evaluation metrics for unsupervised learning algorithms by Palacio-Niño & Berzal (2019) gives an overview of some common metri | Performance metrics to evaluate unsupervised learning
The most voted answer is very helpful, I just want to add something here. Evaluation metrics for unsupervised learning algorithms by Palacio-Niño & Berzal (2019) gives an overview of some common metrics for evaluating unsupervised learning tasks. Both internal and e... | Performance metrics to evaluate unsupervised learning
The most voted answer is very helpful, I just want to add something here. Evaluation metrics for unsupervised learning algorithms by Palacio-Niño & Berzal (2019) gives an overview of some common metri |
2,802 | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, and random effects in mixed models? | Connection between James–Stein estimator and ridge regression
Let $\mathbf y$ be a vector of observation of $\boldsymbol \theta$ of length $m$, ${\mathbf y} \sim N({\boldsymbol \theta}, \sigma^2 I)$, the James-Stein estimator is,
$$\widehat{\boldsymbol \theta}_{JS} =
\left( 1 - \frac{(m-2) \sigma^2}{\|{\mathbf y}\|^2}... | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, | Connection between James–Stein estimator and ridge regression
Let $\mathbf y$ be a vector of observation of $\boldsymbol \theta$ of length $m$, ${\mathbf y} \sim N({\boldsymbol \theta}, \sigma^2 I)$, | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, and random effects in mixed models?
Connection between James–Stein estimator and ridge regression
Let $\mathbf y$ be a vector of observation of $\boldsymbol \theta$ of length $m$, ${\mathbf y} \sim N({\boldsymbol \theta}... | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression,
Connection between James–Stein estimator and ridge regression
Let $\mathbf y$ be a vector of observation of $\boldsymbol \theta$ of length $m$, ${\mathbf y} \sim N({\boldsymbol \theta}, \sigma^2 I)$, |
2,803 | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, and random effects in mixed models? | I'm going to leave it as an exercise for the community to flesh this answer out, but in general the reason why shrinkage estimators will *dominate*$^1$ unbiased estimators in finite samples is because Bayes$^2$ estimators cannot be dominated$^3$, and many shrinkage estimators can be derived as being Bayes.$^4$
All of t... | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, | I'm going to leave it as an exercise for the community to flesh this answer out, but in general the reason why shrinkage estimators will *dominate*$^1$ unbiased estimators in finite samples is because | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, and random effects in mixed models?
I'm going to leave it as an exercise for the community to flesh this answer out, but in general the reason why shrinkage estimators will *dominate*$^1$ unbiased estimators in finite sa... | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression,
I'm going to leave it as an exercise for the community to flesh this answer out, but in general the reason why shrinkage estimators will *dominate*$^1$ unbiased estimators in finite samples is because |
2,804 | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, and random effects in mixed models? | James-Stein assumes that the dimension of response is at least 3. In the standard ridge regression the response is one-dimensional. You are confusing the number of predictors with the response dimension.
That being said, I see the similarity among those situations, but what exactly to do, e.g. whether a factor should b... | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, | James-Stein assumes that the dimension of response is at least 3. In the standard ridge regression the response is one-dimensional. You are confusing the number of predictors with the response dimensi | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, and random effects in mixed models?
James-Stein assumes that the dimension of response is at least 3. In the standard ridge regression the response is one-dimensional. You are confusing the number of predictors with the ... | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression,
James-Stein assumes that the dimension of response is at least 3. In the standard ridge regression the response is one-dimensional. You are confusing the number of predictors with the response dimensi |
2,805 | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, and random effects in mixed models? | As others have said, the connection between the three is how you incorporate the prior information into the measurement.
In case of the Stein paradox, you know that the true correlation between the input variables should be zero (and all the possible correlation measures, since you want to imply independence, not jus... | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, | As others have said, the connection between the three is how you incorporate the prior information into the measurement.
In case of the Stein paradox, you know that the true correlation between the | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, and random effects in mixed models?
As others have said, the connection between the three is how you incorporate the prior information into the measurement.
In case of the Stein paradox, you know that the true correlat... | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression,
As others have said, the connection between the three is how you incorporate the prior information into the measurement.
In case of the Stein paradox, you know that the true correlation between the |
2,806 | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, and random effects in mixed models? | James Stein estimator and Ridge regression
Consider
$\mathbf y=\mathbf{X}\beta+\mathbf{\epsilon}$
With
$\mathbf{\epsilon}\sim N(0,\sigma^2I)$
Least square solution is of the form
$\hat \beta= \mathbf S^{-1}\mathbf{X}'\mathbf{y}$ where $\mathbf S= \mathbf X'\mathbf X$.
$\hat \beta $ is unbiased for $\beta$ and has co... | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, | James Stein estimator and Ridge regression
Consider
$\mathbf y=\mathbf{X}\beta+\mathbf{\epsilon}$
With
$\mathbf{\epsilon}\sim N(0,\sigma^2I)$
Least square solution is of the form
$\hat \beta= \mathb | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, and random effects in mixed models?
James Stein estimator and Ridge regression
Consider
$\mathbf y=\mathbf{X}\beta+\mathbf{\epsilon}$
With
$\mathbf{\epsilon}\sim N(0,\sigma^2I)$
Least square solution is of the form
$\h... | Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression,
James Stein estimator and Ridge regression
Consider
$\mathbf y=\mathbf{X}\beta+\mathbf{\epsilon}$
With
$\mathbf{\epsilon}\sim N(0,\sigma^2I)$
Least square solution is of the form
$\hat \beta= \mathb |
2,807 | Practical thoughts on explanatory vs. predictive modeling | In one sentence
Predictive modelling is all about "what is likely to happen?", whereas explanatory modelling is all about "what can we do about it?"
In many sentences
I think the main difference is what is intended to be done with the analysis. I would suggest explanation is much more important for intervention than p... | Practical thoughts on explanatory vs. predictive modeling | In one sentence
Predictive modelling is all about "what is likely to happen?", whereas explanatory modelling is all about "what can we do about it?"
In many sentences
I think the main difference is wh | Practical thoughts on explanatory vs. predictive modeling
In one sentence
Predictive modelling is all about "what is likely to happen?", whereas explanatory modelling is all about "what can we do about it?"
In many sentences
I think the main difference is what is intended to be done with the analysis. I would suggest ... | Practical thoughts on explanatory vs. predictive modeling
In one sentence
Predictive modelling is all about "what is likely to happen?", whereas explanatory modelling is all about "what can we do about it?"
In many sentences
I think the main difference is wh |
2,808 | Practical thoughts on explanatory vs. predictive modeling | In my view the differences are as follows:
Explanatory/Descriptive
When seeking an explanatory/descriptive answer the primary focus is on the data we have and we seek to discover the underlying relationships between the data after noise has been accounted for.
Example: Is it true that exercising regularly (say 30 minut... | Practical thoughts on explanatory vs. predictive modeling | In my view the differences are as follows:
Explanatory/Descriptive
When seeking an explanatory/descriptive answer the primary focus is on the data we have and we seek to discover the underlying relati | Practical thoughts on explanatory vs. predictive modeling
In my view the differences are as follows:
Explanatory/Descriptive
When seeking an explanatory/descriptive answer the primary focus is on the data we have and we seek to discover the underlying relationships between the data after noise has been accounted for.
E... | Practical thoughts on explanatory vs. predictive modeling
In my view the differences are as follows:
Explanatory/Descriptive
When seeking an explanatory/descriptive answer the primary focus is on the data we have and we seek to discover the underlying relati |
2,809 | Practical thoughts on explanatory vs. predictive modeling | One practical issue that arises here is variable selection in modelling. A variable can be an important explanatory variable (e.g., is statistically significant) but may not be useful for predictive purposes (i.e., its inclusion in the model leads to worse predictive accuracy). I see this mistake almost every day in pu... | Practical thoughts on explanatory vs. predictive modeling | One practical issue that arises here is variable selection in modelling. A variable can be an important explanatory variable (e.g., is statistically significant) but may not be useful for predictive p | Practical thoughts on explanatory vs. predictive modeling
One practical issue that arises here is variable selection in modelling. A variable can be an important explanatory variable (e.g., is statistically significant) but may not be useful for predictive purposes (i.e., its inclusion in the model leads to worse predi... | Practical thoughts on explanatory vs. predictive modeling
One practical issue that arises here is variable selection in modelling. A variable can be an important explanatory variable (e.g., is statistically significant) but may not be useful for predictive p |
2,810 | Practical thoughts on explanatory vs. predictive modeling | Although some people find it easiest to think of the distinction in terms of the model/algorithm used (e.g., neural nets=predictive), that is only one particular aspect of the explain/predict distinction. Here is a deck of slides that I use in my data mining course to teach linear regression from both angles. Even with... | Practical thoughts on explanatory vs. predictive modeling | Although some people find it easiest to think of the distinction in terms of the model/algorithm used (e.g., neural nets=predictive), that is only one particular aspect of the explain/predict distinct | Practical thoughts on explanatory vs. predictive modeling
Although some people find it easiest to think of the distinction in terms of the model/algorithm used (e.g., neural nets=predictive), that is only one particular aspect of the explain/predict distinction. Here is a deck of slides that I use in my data mining cou... | Practical thoughts on explanatory vs. predictive modeling
Although some people find it easiest to think of the distinction in terms of the model/algorithm used (e.g., neural nets=predictive), that is only one particular aspect of the explain/predict distinct |
2,811 | Practical thoughts on explanatory vs. predictive modeling | Example: A classic example that I have seen is in the context of predicting human performance.
Self-efficacy (i.e., the degree to which a person thinks that they can perform a task well) is often a strong predictor of task performance. Thus, if you put self-efficacy into a multiple regression along with other variables... | Practical thoughts on explanatory vs. predictive modeling | Example: A classic example that I have seen is in the context of predicting human performance.
Self-efficacy (i.e., the degree to which a person thinks that they can perform a task well) is often a st | Practical thoughts on explanatory vs. predictive modeling
Example: A classic example that I have seen is in the context of predicting human performance.
Self-efficacy (i.e., the degree to which a person thinks that they can perform a task well) is often a strong predictor of task performance. Thus, if you put self-effi... | Practical thoughts on explanatory vs. predictive modeling
Example: A classic example that I have seen is in the context of predicting human performance.
Self-efficacy (i.e., the degree to which a person thinks that they can perform a task well) is often a st |
2,812 | Practical thoughts on explanatory vs. predictive modeling | Statistical Modeling: Two Cultures (2001) by L. Breiman is, perhaps, the best paper on this point. His main conclusions (see also the replies from other prominent statisticians in the end of the document) are as follows:
"Higher predictive accuracy is associated with
more reliable information about the underlying data... | Practical thoughts on explanatory vs. predictive modeling | Statistical Modeling: Two Cultures (2001) by L. Breiman is, perhaps, the best paper on this point. His main conclusions (see also the replies from other prominent statisticians in the end of the docum | Practical thoughts on explanatory vs. predictive modeling
Statistical Modeling: Two Cultures (2001) by L. Breiman is, perhaps, the best paper on this point. His main conclusions (see also the replies from other prominent statisticians in the end of the document) are as follows:
"Higher predictive accuracy is associate... | Practical thoughts on explanatory vs. predictive modeling
Statistical Modeling: Two Cultures (2001) by L. Breiman is, perhaps, the best paper on this point. His main conclusions (see also the replies from other prominent statisticians in the end of the docum |
2,813 | Practical thoughts on explanatory vs. predictive modeling | I haven't read her work beyond the abstract of the linked paper, but my sense is that the distinction between "explanation" and "prediction" should be thrown away and replaced with the distinction between the aims of the practitioner, which are either "causal" or "predictive". In general, I think "explanation" is such ... | Practical thoughts on explanatory vs. predictive modeling | I haven't read her work beyond the abstract of the linked paper, but my sense is that the distinction between "explanation" and "prediction" should be thrown away and replaced with the distinction bet | Practical thoughts on explanatory vs. predictive modeling
I haven't read her work beyond the abstract of the linked paper, but my sense is that the distinction between "explanation" and "prediction" should be thrown away and replaced with the distinction between the aims of the practitioner, which are either "causal" o... | Practical thoughts on explanatory vs. predictive modeling
I haven't read her work beyond the abstract of the linked paper, but my sense is that the distinction between "explanation" and "prediction" should be thrown away and replaced with the distinction bet |
2,814 | Practical thoughts on explanatory vs. predictive modeling | I am still a bit unclear as to what the question is. Having said that, to my mind the fundamental difference between predictive and explanatory models is the difference in their focus.
Explanatory Models
By definition explanatory models have as their primary focus the goal of explaining something in the real world. In ... | Practical thoughts on explanatory vs. predictive modeling | I am still a bit unclear as to what the question is. Having said that, to my mind the fundamental difference between predictive and explanatory models is the difference in their focus.
Explanatory Mod | Practical thoughts on explanatory vs. predictive modeling
I am still a bit unclear as to what the question is. Having said that, to my mind the fundamental difference between predictive and explanatory models is the difference in their focus.
Explanatory Models
By definition explanatory models have as their primary foc... | Practical thoughts on explanatory vs. predictive modeling
I am still a bit unclear as to what the question is. Having said that, to my mind the fundamental difference between predictive and explanatory models is the difference in their focus.
Explanatory Mod |
2,815 | Practical thoughts on explanatory vs. predictive modeling | as others have already said, the distinction is somewhat meaningless, except in so far as the aims of the researcher are concerned.
Brad Efron, one of the commentators on The Two Cultures paper, made the following observation (as discussed in my earlier question):
Prediction by itself is
only occasionally sufficien... | Practical thoughts on explanatory vs. predictive modeling | as others have already said, the distinction is somewhat meaningless, except in so far as the aims of the researcher are concerned.
Brad Efron, one of the commentators on The Two Cultures paper, made | Practical thoughts on explanatory vs. predictive modeling
as others have already said, the distinction is somewhat meaningless, except in so far as the aims of the researcher are concerned.
Brad Efron, one of the commentators on The Two Cultures paper, made the following observation (as discussed in my earlier questio... | Practical thoughts on explanatory vs. predictive modeling
as others have already said, the distinction is somewhat meaningless, except in so far as the aims of the researcher are concerned.
Brad Efron, one of the commentators on The Two Cultures paper, made |
2,816 | Practical thoughts on explanatory vs. predictive modeling | With respect, this question could be better focused. Have people ever used one term when the other was more appropriate? Yes, of course. Sometimes it's clear enough from context, or you don't want to be pedantic. Sometimes people are just sloppy or lazy in their terminology. This is true of many people, and I'm ce... | Practical thoughts on explanatory vs. predictive modeling | With respect, this question could be better focused. Have people ever used one term when the other was more appropriate? Yes, of course. Sometimes it's clear enough from context, or you don't want | Practical thoughts on explanatory vs. predictive modeling
With respect, this question could be better focused. Have people ever used one term when the other was more appropriate? Yes, of course. Sometimes it's clear enough from context, or you don't want to be pedantic. Sometimes people are just sloppy or lazy in t... | Practical thoughts on explanatory vs. predictive modeling
With respect, this question could be better focused. Have people ever used one term when the other was more appropriate? Yes, of course. Sometimes it's clear enough from context, or you don't want |
2,817 | Practical thoughts on explanatory vs. predictive modeling | Most of the answers have helped clarify what modeling for explanation and modeling for prediction are and why they differ. What is not clear, thus far, is how they differ. So, I thought I would offer an example that might be useful.
Suppose we are intereted in modeling College GPA as a function of academic preparatio... | Practical thoughts on explanatory vs. predictive modeling | Most of the answers have helped clarify what modeling for explanation and modeling for prediction are and why they differ. What is not clear, thus far, is how they differ. So, I thought I would offe | Practical thoughts on explanatory vs. predictive modeling
Most of the answers have helped clarify what modeling for explanation and modeling for prediction are and why they differ. What is not clear, thus far, is how they differ. So, I thought I would offer an example that might be useful.
Suppose we are intereted in... | Practical thoughts on explanatory vs. predictive modeling
Most of the answers have helped clarify what modeling for explanation and modeling for prediction are and why they differ. What is not clear, thus far, is how they differ. So, I thought I would offe |
2,818 | Practical thoughts on explanatory vs. predictive modeling | I would like to offer a model-centered view on the matter.
Predictive modeling is what happens in most analyses. For example, a
researcher sets up a regression model with a bunch of predictors. The
regression coefficients then represent predictive comparisons between
groups. The predictive aspect comes from the probabi... | Practical thoughts on explanatory vs. predictive modeling | I would like to offer a model-centered view on the matter.
Predictive modeling is what happens in most analyses. For example, a
researcher sets up a regression model with a bunch of predictors. The
re | Practical thoughts on explanatory vs. predictive modeling
I would like to offer a model-centered view on the matter.
Predictive modeling is what happens in most analyses. For example, a
researcher sets up a regression model with a bunch of predictors. The
regression coefficients then represent predictive comparisons be... | Practical thoughts on explanatory vs. predictive modeling
I would like to offer a model-centered view on the matter.
Predictive modeling is what happens in most analyses. For example, a
researcher sets up a regression model with a bunch of predictors. The
re |
2,819 | Practical thoughts on explanatory vs. predictive modeling | We can learn a lot more than we think from Black box "predictive" models. The key is in running different types of sensitivity analyses and simulations to really understand how model OUTPUT is affected by changes in the INPUT space. In this sense even a purely predictive model can provide explanatory insights. This is ... | Practical thoughts on explanatory vs. predictive modeling | We can learn a lot more than we think from Black box "predictive" models. The key is in running different types of sensitivity analyses and simulations to really understand how model OUTPUT is affecte | Practical thoughts on explanatory vs. predictive modeling
We can learn a lot more than we think from Black box "predictive" models. The key is in running different types of sensitivity analyses and simulations to really understand how model OUTPUT is affected by changes in the INPUT space. In this sense even a purely p... | Practical thoughts on explanatory vs. predictive modeling
We can learn a lot more than we think from Black box "predictive" models. The key is in running different types of sensitivity analyses and simulations to really understand how model OUTPUT is affecte |
2,820 | Practical thoughts on explanatory vs. predictive modeling | There is distinction between what she calls explanatory and predictive applications in statistics. She says we should know every time we use one or another which one exactly is being used. She says we often mix them up, hence conflation.
I agree that in social science applications, the distinction is sensible, but in n... | Practical thoughts on explanatory vs. predictive modeling | There is distinction between what she calls explanatory and predictive applications in statistics. She says we should know every time we use one or another which one exactly is being used. She says we | Practical thoughts on explanatory vs. predictive modeling
There is distinction between what she calls explanatory and predictive applications in statistics. She says we should know every time we use one or another which one exactly is being used. She says we often mix them up, hence conflation.
I agree that in social s... | Practical thoughts on explanatory vs. predictive modeling
There is distinction between what she calls explanatory and predictive applications in statistics. She says we should know every time we use one or another which one exactly is being used. She says we |
2,821 | Practical thoughts on explanatory vs. predictive modeling | A Structural Model would give explanation and a predictive model would give prediction. A structural model would have latent variables. A structural model is a simultaneous culmination of regression and factor analysis
The latent variables are manifested in the form of multi collinearity in predictive models (regress... | Practical thoughts on explanatory vs. predictive modeling | A Structural Model would give explanation and a predictive model would give prediction. A structural model would have latent variables. A structural model is a simultaneous culmination of regression a | Practical thoughts on explanatory vs. predictive modeling
A Structural Model would give explanation and a predictive model would give prediction. A structural model would have latent variables. A structural model is a simultaneous culmination of regression and factor analysis
The latent variables are manifested in the... | Practical thoughts on explanatory vs. predictive modeling
A Structural Model would give explanation and a predictive model would give prediction. A structural model would have latent variables. A structural model is a simultaneous culmination of regression a |
2,822 | Practical thoughts on explanatory vs. predictive modeling | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
Explanatory model has also been used in medicine and t... | Practical thoughts on explanatory vs. predictive modeling | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| Practical thoughts on explanatory vs. predictive modeling
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | Practical thoughts on explanatory vs. predictive modeling
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
2,823 | How can adding a 2nd IV make the 1st IV significant? | Although collinearity (of predictor variables) is a possible explanation, I would like to suggest it is not an illuminating explanation because we know collinearity is related to "common information" among the predictors, so there is nothing mysterious or counter-intuitive about the side effect of introducing a second ... | How can adding a 2nd IV make the 1st IV significant? | Although collinearity (of predictor variables) is a possible explanation, I would like to suggest it is not an illuminating explanation because we know collinearity is related to "common information" | How can adding a 2nd IV make the 1st IV significant?
Although collinearity (of predictor variables) is a possible explanation, I would like to suggest it is not an illuminating explanation because we know collinearity is related to "common information" among the predictors, so there is nothing mysterious or counter-int... | How can adding a 2nd IV make the 1st IV significant?
Although collinearity (of predictor variables) is a possible explanation, I would like to suggest it is not an illuminating explanation because we know collinearity is related to "common information" |
2,824 | How can adding a 2nd IV make the 1st IV significant? | It feels like the OP's question can be interpreted in two different ways:
Mathematically, how does OLS work, such that adding an independent variable can change results in an unexpected way?
How can modifying my model by adding one variable change the effect of another, independent variable in the model?
There are se... | How can adding a 2nd IV make the 1st IV significant? | It feels like the OP's question can be interpreted in two different ways:
Mathematically, how does OLS work, such that adding an independent variable can change results in an unexpected way?
How can | How can adding a 2nd IV make the 1st IV significant?
It feels like the OP's question can be interpreted in two different ways:
Mathematically, how does OLS work, such that adding an independent variable can change results in an unexpected way?
How can modifying my model by adding one variable change the effect of anot... | How can adding a 2nd IV make the 1st IV significant?
It feels like the OP's question can be interpreted in two different ways:
Mathematically, how does OLS work, such that adding an independent variable can change results in an unexpected way?
How can |
2,825 | How can adding a 2nd IV make the 1st IV significant? | I think this issue has been discussed before on this site fairly thoroughly, if you just knew where to look. So I will probably add a comment later with some links to other questions, or may edit this to provide a fuller explanation if I can't find any.
There are two basic possibilities: First, the other IV may abs... | How can adding a 2nd IV make the 1st IV significant? | I think this issue has been discussed before on this site fairly thoroughly, if you just knew where to look. So I will probably add a comment later with some links to other questions, or may edit thi | How can adding a 2nd IV make the 1st IV significant?
I think this issue has been discussed before on this site fairly thoroughly, if you just knew where to look. So I will probably add a comment later with some links to other questions, or may edit this to provide a fuller explanation if I can't find any.
There are ... | How can adding a 2nd IV make the 1st IV significant?
I think this issue has been discussed before on this site fairly thoroughly, if you just knew where to look. So I will probably add a comment later with some links to other questions, or may edit thi |
2,826 | How can adding a 2nd IV make the 1st IV significant? | This thread has already three excellent answers (+1 to each). My answer is an extended comment and illustration to the point made by @gung (which took me some time to understand):
There are two basic possibilities: First, the other IV may absorb some of the residual variability and thus increase the power of the stati... | How can adding a 2nd IV make the 1st IV significant? | This thread has already three excellent answers (+1 to each). My answer is an extended comment and illustration to the point made by @gung (which took me some time to understand):
There are two basic | How can adding a 2nd IV make the 1st IV significant?
This thread has already three excellent answers (+1 to each). My answer is an extended comment and illustration to the point made by @gung (which took me some time to understand):
There are two basic possibilities: First, the other IV may absorb some of the residual... | How can adding a 2nd IV make the 1st IV significant?
This thread has already three excellent answers (+1 to each). My answer is an extended comment and illustration to the point made by @gung (which took me some time to understand):
There are two basic |
2,827 | How can adding a 2nd IV make the 1st IV significant? | I don't think any of the answers have explicitly mentioned the mathematical intuition for the orthogonal/uncorrelated case, so I will show this here, but I don't believe this answer will be 100% complete.
Suppose that $x_1$ and $x_2$ are uncorrelated, which implies that their centered versions are orthogonal, i.e., $(x... | How can adding a 2nd IV make the 1st IV significant? | I don't think any of the answers have explicitly mentioned the mathematical intuition for the orthogonal/uncorrelated case, so I will show this here, but I don't believe this answer will be 100% compl | How can adding a 2nd IV make the 1st IV significant?
I don't think any of the answers have explicitly mentioned the mathematical intuition for the orthogonal/uncorrelated case, so I will show this here, but I don't believe this answer will be 100% complete.
Suppose that $x_1$ and $x_2$ are uncorrelated, which implies t... | How can adding a 2nd IV make the 1st IV significant?
I don't think any of the answers have explicitly mentioned the mathematical intuition for the orthogonal/uncorrelated case, so I will show this here, but I don't believe this answer will be 100% compl |
2,828 | F1/Dice-Score vs IoU | You're on the right track.
So a few things right off the bat. From the definition of the two metrics, we have that IoU and F score are always within a factor of 2 of each other:
$$ F/2 \leq IoU \leq F $$
and also that they meet at the extremes of one and zero under the conditions that you would expect (perfect match an... | F1/Dice-Score vs IoU | You're on the right track.
So a few things right off the bat. From the definition of the two metrics, we have that IoU and F score are always within a factor of 2 of each other:
$$ F/2 \leq IoU \leq F | F1/Dice-Score vs IoU
You're on the right track.
So a few things right off the bat. From the definition of the two metrics, we have that IoU and F score are always within a factor of 2 of each other:
$$ F/2 \leq IoU \leq F $$
and also that they meet at the extremes of one and zero under the conditions that you would exp... | F1/Dice-Score vs IoU
You're on the right track.
So a few things right off the bat. From the definition of the two metrics, we have that IoU and F score are always within a factor of 2 of each other:
$$ F/2 \leq IoU \leq F |
2,829 | F1/Dice-Score vs IoU | Yes, they indeed represent different things and have different meaning when looking at the formulas.
However, when you use them as a evaluation measure to compare the performance of different model, you only need to choose one of them.
The reason can be explained by the following evidence:
First, let $$ a = TP,\quad b=... | F1/Dice-Score vs IoU | Yes, they indeed represent different things and have different meaning when looking at the formulas.
However, when you use them as a evaluation measure to compare the performance of different model, y | F1/Dice-Score vs IoU
Yes, they indeed represent different things and have different meaning when looking at the formulas.
However, when you use them as a evaluation measure to compare the performance of different model, you only need to choose one of them.
The reason can be explained by the following evidence:
First, l... | F1/Dice-Score vs IoU
Yes, they indeed represent different things and have different meaning when looking at the formulas.
However, when you use them as a evaluation measure to compare the performance of different model, y |
2,830 | F1/Dice-Score vs IoU | For Nico's answer above, I'm wondering shouldn't IoU be TP/(TP+FP+FN) instead of TP/(TP+FP+TN)? Also shouldn't the Dice score be (TP+TP)/(TP+TP+FP+FN)? | F1/Dice-Score vs IoU | For Nico's answer above, I'm wondering shouldn't IoU be TP/(TP+FP+FN) instead of TP/(TP+FP+TN)? Also shouldn't the Dice score be (TP+TP)/(TP+TP+FP+FN)? | F1/Dice-Score vs IoU
For Nico's answer above, I'm wondering shouldn't IoU be TP/(TP+FP+FN) instead of TP/(TP+FP+TN)? Also shouldn't the Dice score be (TP+TP)/(TP+TP+FP+FN)? | F1/Dice-Score vs IoU
For Nico's answer above, I'm wondering shouldn't IoU be TP/(TP+FP+FN) instead of TP/(TP+FP+TN)? Also shouldn't the Dice score be (TP+TP)/(TP+TP+FP+FN)? |
2,831 | Understanding stratified cross-validation | Stratification seeks to ensure that each fold is representative of all strata of the data. Generally this is done in a supervised way for classification and aims to ensure each class is (approximately) equally represented across each test fold (which are of course combined in a complementary way to form training folds)... | Understanding stratified cross-validation | Stratification seeks to ensure that each fold is representative of all strata of the data. Generally this is done in a supervised way for classification and aims to ensure each class is (approximately | Understanding stratified cross-validation
Stratification seeks to ensure that each fold is representative of all strata of the data. Generally this is done in a supervised way for classification and aims to ensure each class is (approximately) equally represented across each test fold (which are of course combined in a... | Understanding stratified cross-validation
Stratification seeks to ensure that each fold is representative of all strata of the data. Generally this is done in a supervised way for classification and aims to ensure each class is (approximately |
2,832 | Understanding stratified cross-validation | Cross-validation article in Encyclopedia of Database Systems says:
Stratification is the process of rearranging the data as to ensure
each fold is a good representative of the whole. For example in a
binary classification problem where each class comprises 50% of the
data, it is best to arrange the data such tha... | Understanding stratified cross-validation | Cross-validation article in Encyclopedia of Database Systems says:
Stratification is the process of rearranging the data as to ensure
each fold is a good representative of the whole. For example in | Understanding stratified cross-validation
Cross-validation article in Encyclopedia of Database Systems says:
Stratification is the process of rearranging the data as to ensure
each fold is a good representative of the whole. For example in a
binary classification problem where each class comprises 50% of the
dat... | Understanding stratified cross-validation
Cross-validation article in Encyclopedia of Database Systems says:
Stratification is the process of rearranging the data as to ensure
each fold is a good representative of the whole. For example in |
2,833 | Understanding stratified cross-validation | A quick and dirty explanation as follows:
Cross Validation: Splits the data into k "random" folds
Stratified Cross Valiadtion: Splits the data into k folds, making sure each fold is an appropriate representative of the original data. (class distribution, mean, variance, etc)
Example of 5 fold Cross Validation:
Example... | Understanding stratified cross-validation | A quick and dirty explanation as follows:
Cross Validation: Splits the data into k "random" folds
Stratified Cross Valiadtion: Splits the data into k folds, making sure each fold is an appropriate rep | Understanding stratified cross-validation
A quick and dirty explanation as follows:
Cross Validation: Splits the data into k "random" folds
Stratified Cross Valiadtion: Splits the data into k folds, making sure each fold is an appropriate representative of the original data. (class distribution, mean, variance, etc)
Ex... | Understanding stratified cross-validation
A quick and dirty explanation as follows:
Cross Validation: Splits the data into k "random" folds
Stratified Cross Valiadtion: Splits the data into k folds, making sure each fold is an appropriate rep |
2,834 | Understanding stratified cross-validation | The mean response value is approximately equal in all the folds is another way of saying the proportion of each class in all the folds are approximately equal.
For example, we have a dataset with 80 class 0 records and 20 class 1 records. We may gain a mean response value of (80*0+20*1)/100 = 0.2 and we want 0.2 to be ... | Understanding stratified cross-validation | The mean response value is approximately equal in all the folds is another way of saying the proportion of each class in all the folds are approximately equal.
For example, we have a dataset with 80 c | Understanding stratified cross-validation
The mean response value is approximately equal in all the folds is another way of saying the proportion of each class in all the folds are approximately equal.
For example, we have a dataset with 80 class 0 records and 20 class 1 records. We may gain a mean response value of (8... | Understanding stratified cross-validation
The mean response value is approximately equal in all the folds is another way of saying the proportion of each class in all the folds are approximately equal.
For example, we have a dataset with 80 c |
2,835 | Understanding stratified cross-validation | This page of the documentation of scikit-learn has a pretty nice visual explanation of what are the differences between cross-validation sampling approaches. Here are some images for the methods you asked taken from the mentioned page.
As you can see, with KFold CV you divide the data in equal parts and pick train and ... | Understanding stratified cross-validation | This page of the documentation of scikit-learn has a pretty nice visual explanation of what are the differences between cross-validation sampling approaches. Here are some images for the methods you a | Understanding stratified cross-validation
This page of the documentation of scikit-learn has a pretty nice visual explanation of what are the differences between cross-validation sampling approaches. Here are some images for the methods you asked taken from the mentioned page.
As you can see, with KFold CV you divide t... | Understanding stratified cross-validation
This page of the documentation of scikit-learn has a pretty nice visual explanation of what are the differences between cross-validation sampling approaches. Here are some images for the methods you a |
2,836 | Why does including latitude and longitude in a GAM account for spatial autocorrelation? | The main issue in any statistical model is the assumptions that underlay any inference procedure. In the sort of model you describe, the residuals are assumed independent. If they have some spatial dependence and this is not modelled in the sytematic part of the model, the residuals from that model will also exhibit sp... | Why does including latitude and longitude in a GAM account for spatial autocorrelation? | The main issue in any statistical model is the assumptions that underlay any inference procedure. In the sort of model you describe, the residuals are assumed independent. If they have some spatial de | Why does including latitude and longitude in a GAM account for spatial autocorrelation?
The main issue in any statistical model is the assumptions that underlay any inference procedure. In the sort of model you describe, the residuals are assumed independent. If they have some spatial dependence and this is not modelle... | Why does including latitude and longitude in a GAM account for spatial autocorrelation?
The main issue in any statistical model is the assumptions that underlay any inference procedure. In the sort of model you describe, the residuals are assumed independent. If they have some spatial de |
2,837 | Why does including latitude and longitude in a GAM account for spatial autocorrelation? | "Spatial autocorrelation" means various things to various people. An overarching concept, though, is that a phenomenon observed at locations $\mathbf{z}$ may depend in some definite way on (a) covariates, (b) location, and (c) its values at nearby locations. (Where the technical definitions vary lie in the kind of dat... | Why does including latitude and longitude in a GAM account for spatial autocorrelation? | "Spatial autocorrelation" means various things to various people. An overarching concept, though, is that a phenomenon observed at locations $\mathbf{z}$ may depend in some definite way on (a) covaria | Why does including latitude and longitude in a GAM account for spatial autocorrelation?
"Spatial autocorrelation" means various things to various people. An overarching concept, though, is that a phenomenon observed at locations $\mathbf{z}$ may depend in some definite way on (a) covariates, (b) location, and (c) its v... | Why does including latitude and longitude in a GAM account for spatial autocorrelation?
"Spatial autocorrelation" means various things to various people. An overarching concept, though, is that a phenomenon observed at locations $\mathbf{z}$ may depend in some definite way on (a) covaria |
2,838 | Why does including latitude and longitude in a GAM account for spatial autocorrelation? | The other answers are good I just wanted to add something about 'accounting for' spatial autocorrelation. Sometimes this claim is made more strongly along the lines of "accounting for spatial autocorrelation not explained by the covariates".
This can present a misleading picture of what the spatial smooth does. It is... | Why does including latitude and longitude in a GAM account for spatial autocorrelation? | The other answers are good I just wanted to add something about 'accounting for' spatial autocorrelation. Sometimes this claim is made more strongly along the lines of "accounting for spatial autocor | Why does including latitude and longitude in a GAM account for spatial autocorrelation?
The other answers are good I just wanted to add something about 'accounting for' spatial autocorrelation. Sometimes this claim is made more strongly along the lines of "accounting for spatial autocorrelation not explained by the co... | Why does including latitude and longitude in a GAM account for spatial autocorrelation?
The other answers are good I just wanted to add something about 'accounting for' spatial autocorrelation. Sometimes this claim is made more strongly along the lines of "accounting for spatial autocor |
2,839 | Why does including latitude and longitude in a GAM account for spatial autocorrelation? | Spatial correlation is simply how the x and y coordinates relate to the magnitude of the resulting surface in the space. So the autocorrelation between the coordinates can be expressed in terms of a functional relationship between the neighboring points. | Why does including latitude and longitude in a GAM account for spatial autocorrelation? | Spatial correlation is simply how the x and y coordinates relate to the magnitude of the resulting surface in the space. So the autocorrelation between the coordinates can be expressed in terms of a | Why does including latitude and longitude in a GAM account for spatial autocorrelation?
Spatial correlation is simply how the x and y coordinates relate to the magnitude of the resulting surface in the space. So the autocorrelation between the coordinates can be expressed in terms of a functional relationship between ... | Why does including latitude and longitude in a GAM account for spatial autocorrelation?
Spatial correlation is simply how the x and y coordinates relate to the magnitude of the resulting surface in the space. So the autocorrelation between the coordinates can be expressed in terms of a |
2,840 | Variance of product of multiple independent random variables | I will assume that the random variables $X_1, X_2, \cdots , X_n$ are independent,
which condition the OP has not included in the problem statement. With this
assumption, we have that
$$\begin{align}
\operatorname{var}(X_1\cdots X_n)
&= E[(X_1\cdots X_n)^2]-\left(E[X_1\cdots X_n]\right)^2\\
&= E[X_1^2\cdots X_n^2]-\le... | Variance of product of multiple independent random variables | I will assume that the random variables $X_1, X_2, \cdots , X_n$ are independent,
which condition the OP has not included in the problem statement. With this
assumption, we have that
$$\begin{align}
| Variance of product of multiple independent random variables
I will assume that the random variables $X_1, X_2, \cdots , X_n$ are independent,
which condition the OP has not included in the problem statement. With this
assumption, we have that
$$\begin{align}
\operatorname{var}(X_1\cdots X_n)
&= E[(X_1\cdots X_n)^2]-... | Variance of product of multiple independent random variables
I will assume that the random variables $X_1, X_2, \cdots , X_n$ are independent,
which condition the OP has not included in the problem statement. With this
assumption, we have that
$$\begin{align}
|
2,841 | Covariance and independence? | Easy example: Let $X$ be a random variable that is $-1$ or $+1$ with probability 0.5. Then let $Y$ be a random variable such that $Y=0$ if $X=-1$, and $Y$ is randomly $-1$ or $+1$ with probability 0.5 if $X=1$.
Clearly $X$ and $Y$ are highly dependent (since knowing $Y$ allows me to perfectly know $X$), but their cov... | Covariance and independence? | Easy example: Let $X$ be a random variable that is $-1$ or $+1$ with probability 0.5. Then let $Y$ be a random variable such that $Y=0$ if $X=-1$, and $Y$ is randomly $-1$ or $+1$ with probability 0 | Covariance and independence?
Easy example: Let $X$ be a random variable that is $-1$ or $+1$ with probability 0.5. Then let $Y$ be a random variable such that $Y=0$ if $X=-1$, and $Y$ is randomly $-1$ or $+1$ with probability 0.5 if $X=1$.
Clearly $X$ and $Y$ are highly dependent (since knowing $Y$ allows me to perfe... | Covariance and independence?
Easy example: Let $X$ be a random variable that is $-1$ or $+1$ with probability 0.5. Then let $Y$ be a random variable such that $Y=0$ if $X=-1$, and $Y$ is randomly $-1$ or $+1$ with probability 0 |
2,842 | Covariance and independence? | Here is the example I always give to the students. Take a random variable $X$ with $E[X]=0$ and $E[X^3]=0$, e.g. normal random variable with zero mean. Take $Y=X^2$. It is clear that $X$ and $Y$ are related, but
$$Cov(X,Y)=E[XY]-E[X]\cdot E[Y]=E[X^3]=0.$$ | Covariance and independence? | Here is the example I always give to the students. Take a random variable $X$ with $E[X]=0$ and $E[X^3]=0$, e.g. normal random variable with zero mean. Take $Y=X^2$. It is clear that $X$ and $Y$ are | Covariance and independence?
Here is the example I always give to the students. Take a random variable $X$ with $E[X]=0$ and $E[X^3]=0$, e.g. normal random variable with zero mean. Take $Y=X^2$. It is clear that $X$ and $Y$ are related, but
$$Cov(X,Y)=E[XY]-E[X]\cdot E[Y]=E[X^3]=0.$$ | Covariance and independence?
Here is the example I always give to the students. Take a random variable $X$ with $E[X]=0$ and $E[X^3]=0$, e.g. normal random variable with zero mean. Take $Y=X^2$. It is clear that $X$ and $Y$ are |
2,843 | Covariance and independence? | The image below (source Wikipedia) has a number of examples on the third row, in particular the first and the fourth example have a strong dependent relationship, but 0 correlation (and 0 covariance). | Covariance and independence? | The image below (source Wikipedia) has a number of examples on the third row, in particular the first and the fourth example have a strong dependent relationship, but 0 correlation (and 0 covariance). | Covariance and independence?
The image below (source Wikipedia) has a number of examples on the third row, in particular the first and the fourth example have a strong dependent relationship, but 0 correlation (and 0 covariance). | Covariance and independence?
The image below (source Wikipedia) has a number of examples on the third row, in particular the first and the fourth example have a strong dependent relationship, but 0 correlation (and 0 covariance). |
2,844 | Covariance and independence? | Some other examples, consider datapoints that form a circle or ellipse, the covariance is 0, but knowing x you narrow y to 2 values. Or data in a square or rectangle. Also data that forms an X or a V or a ^ or < or > will all give covariance 0, but are not independent. If y = sin(x) (or cos) and x covers an integer ... | Covariance and independence? | Some other examples, consider datapoints that form a circle or ellipse, the covariance is 0, but knowing x you narrow y to 2 values. Or data in a square or rectangle. Also data that forms an X or a | Covariance and independence?
Some other examples, consider datapoints that form a circle or ellipse, the covariance is 0, but knowing x you narrow y to 2 values. Or data in a square or rectangle. Also data that forms an X or a V or a ^ or < or > will all give covariance 0, but are not independent. If y = sin(x) (or ... | Covariance and independence?
Some other examples, consider datapoints that form a circle or ellipse, the covariance is 0, but knowing x you narrow y to 2 values. Or data in a square or rectangle. Also data that forms an X or a |
2,845 | Covariance and independence? | Inspired by mpiktas's answer.
Consider $X$ to be a uniformly distributed random variable, i.e. $X \sim U(-1,1) $. Here, $$E[X] = (b+a)/2 = 0.$$ $$E[X^2] = \int_{-1}^{1} x^2 dx = 2/3$$ $$E[X^3] = \int_{-1}^{1} x^3 dx = 0$$
Since $Cov(X, Y) = E[XY] - E[X] \cdot E[Y]$, $$ Cov(X^2, X) = E[X^3] - E[X] \cdot E[X^2] \\ = 0 -... | Covariance and independence? | Inspired by mpiktas's answer.
Consider $X$ to be a uniformly distributed random variable, i.e. $X \sim U(-1,1) $. Here, $$E[X] = (b+a)/2 = 0.$$ $$E[X^2] = \int_{-1}^{1} x^2 dx = 2/3$$ $$E[X^3] = \int | Covariance and independence?
Inspired by mpiktas's answer.
Consider $X$ to be a uniformly distributed random variable, i.e. $X \sim U(-1,1) $. Here, $$E[X] = (b+a)/2 = 0.$$ $$E[X^2] = \int_{-1}^{1} x^2 dx = 2/3$$ $$E[X^3] = \int_{-1}^{1} x^3 dx = 0$$
Since $Cov(X, Y) = E[XY] - E[X] \cdot E[Y]$, $$ Cov(X^2, X) = E[X^3]... | Covariance and independence?
Inspired by mpiktas's answer.
Consider $X$ to be a uniformly distributed random variable, i.e. $X \sim U(-1,1) $. Here, $$E[X] = (b+a)/2 = 0.$$ $$E[X^2] = \int_{-1}^{1} x^2 dx = 2/3$$ $$E[X^3] = \int |
2,846 | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?) | There's a good reason for it.
The value can be found via noquote(unlist(format(.Machine)))
double.eps double.neg.eps double.xmin
2.220446e-16 1.110223e-16 2.225074e-308
double.xmax double.base double.digits
1.797693e+308 ... | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?) | There's a good reason for it.
The value can be found via noquote(unlist(format(.Machine)))
double.eps double.neg.eps double.xmin
2.220446e-16 1.110223e-1 | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?)
There's a good reason for it.
The value can be found via noquote(unlist(format(.Machine)))
double.eps double.neg.eps double.xmin
2.220446e-16 1.110223e-16 2.225074e-308
d... | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?)
There's a good reason for it.
The value can be found via noquote(unlist(format(.Machine)))
double.eps double.neg.eps double.xmin
2.220446e-16 1.110223e-1 |
2,847 | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?) | What common practice is might depend on your field of research. The manual of the American Psychological Association (APA), which is one of the most often used citation styles, states (p. 139, 6th edition):
Do not use any value smaller than p < 0.001 | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?) | What common practice is might depend on your field of research. The manual of the American Psychological Association (APA), which is one of the most often used citation styles, states (p. 139, 6th edi | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?)
What common practice is might depend on your field of research. The manual of the American Psychological Association (APA), which is one of the most often used citation styles, states (p. 139, 6th edition):
Do not use any value smalle... | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?)
What common practice is might depend on your field of research. The manual of the American Psychological Association (APA), which is one of the most often used citation styles, states (p. 139, 6th edi |
2,848 | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?) | Such extreme p-values occur more often in fields with very large amounts of data, such as genomics and process monitoring. In those cases, it's sometimes reported as -log10(p-value). See for example, this figure from Nature, where the p-values go down to 1e-26.
-log10(p-value) is called "LogWorth" by statisticians I wo... | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?) | Such extreme p-values occur more often in fields with very large amounts of data, such as genomics and process monitoring. In those cases, it's sometimes reported as -log10(p-value). See for example, | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?)
Such extreme p-values occur more often in fields with very large amounts of data, such as genomics and process monitoring. In those cases, it's sometimes reported as -log10(p-value). See for example, this figure from Nature, where the p... | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?)
Such extreme p-values occur more often in fields with very large amounts of data, such as genomics and process monitoring. In those cases, it's sometimes reported as -log10(p-value). See for example, |
2,849 | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?) | I'm surprised no one mentioned this term explicitly, but @Glen_b alluded to it. The formal terminology for this issue is "machine epsilon." https://en.wikipedia.org/wiki/Machine_epsilon
For 64 bit double precision, the smallest representable value is $1.11e^{-16}$ or $2.22e^{-16}$ depending on how the software computes... | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?) | I'm surprised no one mentioned this term explicitly, but @Glen_b alluded to it. The formal terminology for this issue is "machine epsilon." https://en.wikipedia.org/wiki/Machine_epsilon
For 64 bit dou | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?)
I'm surprised no one mentioned this term explicitly, but @Glen_b alluded to it. The formal terminology for this issue is "machine epsilon." https://en.wikipedia.org/wiki/Machine_epsilon
For 64 bit double precision, the smallest represen... | How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?)
I'm surprised no one mentioned this term explicitly, but @Glen_b alluded to it. The formal terminology for this issue is "machine epsilon." https://en.wikipedia.org/wiki/Machine_epsilon
For 64 bit dou |
2,850 | Why is multicollinearity not checked in modern statistics/machine learning | Considering multicollineariy is important in regression analysis because, in extrema, it directly bears on whether or not your coefficients are uniquely identified in the data. In less severe cases, it can still mess with your coefficient estimates; small changes in the data used for estimation may cause wild swings in... | Why is multicollinearity not checked in modern statistics/machine learning | Considering multicollineariy is important in regression analysis because, in extrema, it directly bears on whether or not your coefficients are uniquely identified in the data. In less severe cases, i | Why is multicollinearity not checked in modern statistics/machine learning
Considering multicollineariy is important in regression analysis because, in extrema, it directly bears on whether or not your coefficients are uniquely identified in the data. In less severe cases, it can still mess with your coefficient estima... | Why is multicollinearity not checked in modern statistics/machine learning
Considering multicollineariy is important in regression analysis because, in extrema, it directly bears on whether or not your coefficients are uniquely identified in the data. In less severe cases, i |
2,851 | Why is multicollinearity not checked in modern statistics/machine learning | The reason is because the goals of "traditional statistics" are different from many Machine Learning techniques.
By "traditional statistics", I assume you mean regression and its variants. In regression, we are trying to understand the impact the independent variables have on the dependent variable. If there is strong... | Why is multicollinearity not checked in modern statistics/machine learning | The reason is because the goals of "traditional statistics" are different from many Machine Learning techniques.
By "traditional statistics", I assume you mean regression and its variants. In regress | Why is multicollinearity not checked in modern statistics/machine learning
The reason is because the goals of "traditional statistics" are different from many Machine Learning techniques.
By "traditional statistics", I assume you mean regression and its variants. In regression, we are trying to understand the impact t... | Why is multicollinearity not checked in modern statistics/machine learning
The reason is because the goals of "traditional statistics" are different from many Machine Learning techniques.
By "traditional statistics", I assume you mean regression and its variants. In regress |
2,852 | Why is multicollinearity not checked in modern statistics/machine learning | There appears to be an underlying assumption here that not checking for collinearity is a reasonable or even best practice. This seems flawed. For example, checking for perfect collinearity in a dataset with many predictors will reveal whether two variables are actually the same thing e.g. birth date and age (example t... | Why is multicollinearity not checked in modern statistics/machine learning | There appears to be an underlying assumption here that not checking for collinearity is a reasonable or even best practice. This seems flawed. For example, checking for perfect collinearity in a datas | Why is multicollinearity not checked in modern statistics/machine learning
There appears to be an underlying assumption here that not checking for collinearity is a reasonable or even best practice. This seems flawed. For example, checking for perfect collinearity in a dataset with many predictors will reveal whether t... | Why is multicollinearity not checked in modern statistics/machine learning
There appears to be an underlying assumption here that not checking for collinearity is a reasonable or even best practice. This seems flawed. For example, checking for perfect collinearity in a datas |
2,853 | Why is multicollinearity not checked in modern statistics/machine learning | The main issue with multicollinearity is that it messes up the coefficients (betas) of independent variables. That's why it's a serious issue when you're studying the relationships between variables, establishing causality etc.
However, if you're not interested in understanding the phenomenon so much, but are solely fo... | Why is multicollinearity not checked in modern statistics/machine learning | The main issue with multicollinearity is that it messes up the coefficients (betas) of independent variables. That's why it's a serious issue when you're studying the relationships between variables, | Why is multicollinearity not checked in modern statistics/machine learning
The main issue with multicollinearity is that it messes up the coefficients (betas) of independent variables. That's why it's a serious issue when you're studying the relationships between variables, establishing causality etc.
However, if you'r... | Why is multicollinearity not checked in modern statistics/machine learning
The main issue with multicollinearity is that it messes up the coefficients (betas) of independent variables. That's why it's a serious issue when you're studying the relationships between variables, |
2,854 | Why is multicollinearity not checked in modern statistics/machine learning | The regularization in those machine learning stabilizes the regression coefficients, so at least that effect of multicollinearity tamed. But more importantly, if you're going for prediction (which machine learners often are), then the multicollinearity "problem" wasn't that big of a problem in the first place. It's a p... | Why is multicollinearity not checked in modern statistics/machine learning | The regularization in those machine learning stabilizes the regression coefficients, so at least that effect of multicollinearity tamed. But more importantly, if you're going for prediction (which mac | Why is multicollinearity not checked in modern statistics/machine learning
The regularization in those machine learning stabilizes the regression coefficients, so at least that effect of multicollinearity tamed. But more importantly, if you're going for prediction (which machine learners often are), then the multicolli... | Why is multicollinearity not checked in modern statistics/machine learning
The regularization in those machine learning stabilizes the regression coefficients, so at least that effect of multicollinearity tamed. But more importantly, if you're going for prediction (which mac |
2,855 | Why is multicollinearity not checked in modern statistics/machine learning | I think that multicollinearity should be checked in machine learning. Here is why: Suppose that you have two highly correlated features X and Y in our dataset. This means that the response plane is not reliable (a small change in the data can have drastic effects on the orientation of the response plane). Which implies... | Why is multicollinearity not checked in modern statistics/machine learning | I think that multicollinearity should be checked in machine learning. Here is why: Suppose that you have two highly correlated features X and Y in our dataset. This means that the response plane is no | Why is multicollinearity not checked in modern statistics/machine learning
I think that multicollinearity should be checked in machine learning. Here is why: Suppose that you have two highly correlated features X and Y in our dataset. This means that the response plane is not reliable (a small change in the data can ha... | Why is multicollinearity not checked in modern statistics/machine learning
I think that multicollinearity should be checked in machine learning. Here is why: Suppose that you have two highly correlated features X and Y in our dataset. This means that the response plane is no |
2,856 | Why would parametric statistics ever be preferred over nonparametric? | Rarely if ever a parametric test and a non-parametric test actually have the same null. The parametric $t$-test is testing the mean of the distribution, assuming the first two moments exist. The Wilcoxon rank sum test does not assume any moments, and tests equality of distributions instead. Its implied parameter is a w... | Why would parametric statistics ever be preferred over nonparametric? | Rarely if ever a parametric test and a non-parametric test actually have the same null. The parametric $t$-test is testing the mean of the distribution, assuming the first two moments exist. The Wilco | Why would parametric statistics ever be preferred over nonparametric?
Rarely if ever a parametric test and a non-parametric test actually have the same null. The parametric $t$-test is testing the mean of the distribution, assuming the first two moments exist. The Wilcoxon rank sum test does not assume any moments, and... | Why would parametric statistics ever be preferred over nonparametric?
Rarely if ever a parametric test and a non-parametric test actually have the same null. The parametric $t$-test is testing the mean of the distribution, assuming the first two moments exist. The Wilco |
2,857 | Why would parametric statistics ever be preferred over nonparametric? | As others have written: if the preconditions are met, your parametric test will be more powerful than the nonparametric one.
In your watch analogy, the non-water-resistant one would be far more accurate unless it got wet. For instance, your water-resistant watch might be off by one hour either way, whereas the non-wate... | Why would parametric statistics ever be preferred over nonparametric? | As others have written: if the preconditions are met, your parametric test will be more powerful than the nonparametric one.
In your watch analogy, the non-water-resistant one would be far more accura | Why would parametric statistics ever be preferred over nonparametric?
As others have written: if the preconditions are met, your parametric test will be more powerful than the nonparametric one.
In your watch analogy, the non-water-resistant one would be far more accurate unless it got wet. For instance, your water-res... | Why would parametric statistics ever be preferred over nonparametric?
As others have written: if the preconditions are met, your parametric test will be more powerful than the nonparametric one.
In your watch analogy, the non-water-resistant one would be far more accura |
2,858 | Why would parametric statistics ever be preferred over nonparametric? | While I agree that in many cases, non-parametric techniques are favourable, there are also situations in which parametric methods are more useful.
Let's focus on the "two-sample t-test versus Wilcoxon's rank sum test" discussion (otherwise we have to write a whole book).
With tiny group sizes of 2-3, only the t-test ... | Why would parametric statistics ever be preferred over nonparametric? | While I agree that in many cases, non-parametric techniques are favourable, there are also situations in which parametric methods are more useful.
Let's focus on the "two-sample t-test versus Wilcoxo | Why would parametric statistics ever be preferred over nonparametric?
While I agree that in many cases, non-parametric techniques are favourable, there are also situations in which parametric methods are more useful.
Let's focus on the "two-sample t-test versus Wilcoxon's rank sum test" discussion (otherwise we have t... | Why would parametric statistics ever be preferred over nonparametric?
While I agree that in many cases, non-parametric techniques are favourable, there are also situations in which parametric methods are more useful.
Let's focus on the "two-sample t-test versus Wilcoxo |
2,859 | Why would parametric statistics ever be preferred over nonparametric? | In hypothesis testing nonparametric tests are often testing different hypotheses, which is one reason why one can't always just substitute a nonparametric test for a parametric one.
More generally, parametric procedures provide a way of imposing structure on otherwise unstructured problems. This is very useful and can... | Why would parametric statistics ever be preferred over nonparametric? | In hypothesis testing nonparametric tests are often testing different hypotheses, which is one reason why one can't always just substitute a nonparametric test for a parametric one.
More generally, pa | Why would parametric statistics ever be preferred over nonparametric?
In hypothesis testing nonparametric tests are often testing different hypotheses, which is one reason why one can't always just substitute a nonparametric test for a parametric one.
More generally, parametric procedures provide a way of imposing stru... | Why would parametric statistics ever be preferred over nonparametric?
In hypothesis testing nonparametric tests are often testing different hypotheses, which is one reason why one can't always just substitute a nonparametric test for a parametric one.
More generally, pa |
2,860 | Why would parametric statistics ever be preferred over nonparametric? | Semiparametric models have many advantages. They offer tests such as the Wilcoxon test as a special case, but allow estimation of effect ratios, quantiles, means, and exceedance probabilities. They extend to longitudinal and censored data. They are robust in the Y-space and are transformation invariant except for es... | Why would parametric statistics ever be preferred over nonparametric? | Semiparametric models have many advantages. They offer tests such as the Wilcoxon test as a special case, but allow estimation of effect ratios, quantiles, means, and exceedance probabilities. They | Why would parametric statistics ever be preferred over nonparametric?
Semiparametric models have many advantages. They offer tests such as the Wilcoxon test as a special case, but allow estimation of effect ratios, quantiles, means, and exceedance probabilities. They extend to longitudinal and censored data. They ar... | Why would parametric statistics ever be preferred over nonparametric?
Semiparametric models have many advantages. They offer tests such as the Wilcoxon test as a special case, but allow estimation of effect ratios, quantiles, means, and exceedance probabilities. They |
2,861 | Why would parametric statistics ever be preferred over nonparametric? | Among the host of answers supplied, I would also call attention to Bayesian statistics. Some problems cannot be answered by likelihoods alone. A Frequentist uses counterfactual reasoning where the "probability" refers to alternate universes and an alternate universe framework makes no sense as far as inferring the stat... | Why would parametric statistics ever be preferred over nonparametric? | Among the host of answers supplied, I would also call attention to Bayesian statistics. Some problems cannot be answered by likelihoods alone. A Frequentist uses counterfactual reasoning where the "pr | Why would parametric statistics ever be preferred over nonparametric?
Among the host of answers supplied, I would also call attention to Bayesian statistics. Some problems cannot be answered by likelihoods alone. A Frequentist uses counterfactual reasoning where the "probability" refers to alternate universes and an al... | Why would parametric statistics ever be preferred over nonparametric?
Among the host of answers supplied, I would also call attention to Bayesian statistics. Some problems cannot be answered by likelihoods alone. A Frequentist uses counterfactual reasoning where the "pr |
2,862 | Why would parametric statistics ever be preferred over nonparametric? | The only reason I am answering despite all the fine answers above is that no one has called attention to the #1 reason we use parametric tests (at least in particle physics data analysis). Because we know the parametrization of the data. Duh! That's such a big advantage. You're boiling down your hundreds, thousands or ... | Why would parametric statistics ever be preferred over nonparametric? | The only reason I am answering despite all the fine answers above is that no one has called attention to the #1 reason we use parametric tests (at least in particle physics data analysis). Because we | Why would parametric statistics ever be preferred over nonparametric?
The only reason I am answering despite all the fine answers above is that no one has called attention to the #1 reason we use parametric tests (at least in particle physics data analysis). Because we know the parametrization of the data. Duh! That's ... | Why would parametric statistics ever be preferred over nonparametric?
The only reason I am answering despite all the fine answers above is that no one has called attention to the #1 reason we use parametric tests (at least in particle physics data analysis). Because we |
2,863 | Why would parametric statistics ever be preferred over nonparametric? | Nonparametric statistics has its own problems! One of them is the emphasis on hypothesis testing, often we need estimation and confidence intervals, and getting them in complicated models with nonparametrics is --- complicated. There is a very good blog post about this, with discussion, at http://andrewgelman.com/201... | Why would parametric statistics ever be preferred over nonparametric? | Nonparametric statistics has its own problems! One of them is the emphasis on hypothesis testing, often we need estimation and confidence intervals, and getting them in complicated models with nonpar | Why would parametric statistics ever be preferred over nonparametric?
Nonparametric statistics has its own problems! One of them is the emphasis on hypothesis testing, often we need estimation and confidence intervals, and getting them in complicated models with nonparametrics is --- complicated. There is a very good... | Why would parametric statistics ever be preferred over nonparametric?
Nonparametric statistics has its own problems! One of them is the emphasis on hypothesis testing, often we need estimation and confidence intervals, and getting them in complicated models with nonpar |
2,864 | Why would parametric statistics ever be preferred over nonparametric? | I would say that non-parametric statistics are more generally applicable in the sense that they make less assumptions than parametric statistics.
Nevertheless, if one uses a parametric statistics and the underlying assumptions are fulfilled, then the paramatric statistics will be more powerfull than the non-parametri... | Why would parametric statistics ever be preferred over nonparametric? | I would say that non-parametric statistics are more generally applicable in the sense that they make less assumptions than parametric statistics.
Nevertheless, if one uses a parametric statistics an | Why would parametric statistics ever be preferred over nonparametric?
I would say that non-parametric statistics are more generally applicable in the sense that they make less assumptions than parametric statistics.
Nevertheless, if one uses a parametric statistics and the underlying assumptions are fulfilled, then t... | Why would parametric statistics ever be preferred over nonparametric?
I would say that non-parametric statistics are more generally applicable in the sense that they make less assumptions than parametric statistics.
Nevertheless, if one uses a parametric statistics an |
2,865 | Why would parametric statistics ever be preferred over nonparametric? | Parametric statistics are often ways to incorporate external [to data] knowledge. For instance, you know that the error distribution is normal, and this knowledge came from either prior experience or some other consideration and not from the data set. In this case, by assuming normal distribution you are incorporating ... | Why would parametric statistics ever be preferred over nonparametric? | Parametric statistics are often ways to incorporate external [to data] knowledge. For instance, you know that the error distribution is normal, and this knowledge came from either prior experience or | Why would parametric statistics ever be preferred over nonparametric?
Parametric statistics are often ways to incorporate external [to data] knowledge. For instance, you know that the error distribution is normal, and this knowledge came from either prior experience or some other consideration and not from the data set... | Why would parametric statistics ever be preferred over nonparametric?
Parametric statistics are often ways to incorporate external [to data] knowledge. For instance, you know that the error distribution is normal, and this knowledge came from either prior experience or |
2,866 | Why would parametric statistics ever be preferred over nonparametric? | This is not hypothesis testing scenario, but it may be a good example for answering your question: let's consider clustering analysis. There are many "non-parametric" clustering methods like hierarchical clustering, K-means etc., but the problem is always how to assess if your clustering solution is "better", than othe... | Why would parametric statistics ever be preferred over nonparametric? | This is not hypothesis testing scenario, but it may be a good example for answering your question: let's consider clustering analysis. There are many "non-parametric" clustering methods like hierarchi | Why would parametric statistics ever be preferred over nonparametric?
This is not hypothesis testing scenario, but it may be a good example for answering your question: let's consider clustering analysis. There are many "non-parametric" clustering methods like hierarchical clustering, K-means etc., but the problem is a... | Why would parametric statistics ever be preferred over nonparametric?
This is not hypothesis testing scenario, but it may be a good example for answering your question: let's consider clustering analysis. There are many "non-parametric" clustering methods like hierarchi |
2,867 | Why would parametric statistics ever be preferred over nonparametric? | Predictions and forecasting to new data are often very difficult or impossible for non-parametric models. For example, I can forecast the number of warranty claims for the next 10 years using a Weibull or Lognormal survival model, however this is not possible using the Cox model or Kaplan-Meier.
Edit: Let me be a litt... | Why would parametric statistics ever be preferred over nonparametric? | Predictions and forecasting to new data are often very difficult or impossible for non-parametric models. For example, I can forecast the number of warranty claims for the next 10 years using a Weibu | Why would parametric statistics ever be preferred over nonparametric?
Predictions and forecasting to new data are often very difficult or impossible for non-parametric models. For example, I can forecast the number of warranty claims for the next 10 years using a Weibull or Lognormal survival model, however this is no... | Why would parametric statistics ever be preferred over nonparametric?
Predictions and forecasting to new data are often very difficult or impossible for non-parametric models. For example, I can forecast the number of warranty claims for the next 10 years using a Weibu |
2,868 | Why would parametric statistics ever be preferred over nonparametric? | I honestly believe that there is no right answer to this question. Judging from the given answers, the consensus is that parametric tests are more powerful than nonparametric equivalents. I won't contest this view but I see it more as a hypothetical rather than factual viewpoint since it is not something explicitly tau... | Why would parametric statistics ever be preferred over nonparametric? | I honestly believe that there is no right answer to this question. Judging from the given answers, the consensus is that parametric tests are more powerful than nonparametric equivalents. I won't cont | Why would parametric statistics ever be preferred over nonparametric?
I honestly believe that there is no right answer to this question. Judging from the given answers, the consensus is that parametric tests are more powerful than nonparametric equivalents. I won't contest this view but I see it more as a hypothetical ... | Why would parametric statistics ever be preferred over nonparametric?
I honestly believe that there is no right answer to this question. Judging from the given answers, the consensus is that parametric tests are more powerful than nonparametric equivalents. I won't cont |
2,869 | Why would parametric statistics ever be preferred over nonparametric? | Lots of good answers already but there are some reasons I haven't seen mentioned:
Familiarity. Depending on your audience, the parameteric result may be much more familiar than a roughly equivalent non-parametric one. If the two give similar conclusions, then familiarity is good.
Simplicity. Sometimes, the parametric... | Why would parametric statistics ever be preferred over nonparametric? | Lots of good answers already but there are some reasons I haven't seen mentioned:
Familiarity. Depending on your audience, the parameteric result may be much more familiar than a roughly equivalent n | Why would parametric statistics ever be preferred over nonparametric?
Lots of good answers already but there are some reasons I haven't seen mentioned:
Familiarity. Depending on your audience, the parameteric result may be much more familiar than a roughly equivalent non-parametric one. If the two give similar conclus... | Why would parametric statistics ever be preferred over nonparametric?
Lots of good answers already but there are some reasons I haven't seen mentioned:
Familiarity. Depending on your audience, the parameteric result may be much more familiar than a roughly equivalent n |
2,870 | Why would parametric statistics ever be preferred over nonparametric? | It is a an issue of statistical power. Non-parametric tests generally have lower statistical power than their parametric counterparts. | Why would parametric statistics ever be preferred over nonparametric? | It is a an issue of statistical power. Non-parametric tests generally have lower statistical power than their parametric counterparts. | Why would parametric statistics ever be preferred over nonparametric?
It is a an issue of statistical power. Non-parametric tests generally have lower statistical power than their parametric counterparts. | Why would parametric statistics ever be preferred over nonparametric?
It is a an issue of statistical power. Non-parametric tests generally have lower statistical power than their parametric counterparts. |
2,871 | Impractical question: is it possible to find the regression line using a ruler and compass? | Loosely speaking, it's apparently possible to compute any quantity which can be expressed "using only the integers 0 and 1 and the operations for addition, subtraction, multiplication, division, and square roots" with only a compass and ruler -- the wikipedia article on constructible numbers has more details. Since the... | Impractical question: is it possible to find the regression line using a ruler and compass? | Loosely speaking, it's apparently possible to compute any quantity which can be expressed "using only the integers 0 and 1 and the operations for addition, subtraction, multiplication, division, and s | Impractical question: is it possible to find the regression line using a ruler and compass?
Loosely speaking, it's apparently possible to compute any quantity which can be expressed "using only the integers 0 and 1 and the operations for addition, subtraction, multiplication, division, and square roots" with only a com... | Impractical question: is it possible to find the regression line using a ruler and compass?
Loosely speaking, it's apparently possible to compute any quantity which can be expressed "using only the integers 0 and 1 and the operations for addition, subtraction, multiplication, division, and s |
2,872 | Where to cut a dendrogram? | There is no definitive answer since cluster analysis is essentially an exploratory approach; the interpretation of the resulting hierarchical structure is context-dependent and often several solutions are equally good from a theoretical point of view.
Several clues were given in a related question, What stop-criteria f... | Where to cut a dendrogram? | There is no definitive answer since cluster analysis is essentially an exploratory approach; the interpretation of the resulting hierarchical structure is context-dependent and often several solutions | Where to cut a dendrogram?
There is no definitive answer since cluster analysis is essentially an exploratory approach; the interpretation of the resulting hierarchical structure is context-dependent and often several solutions are equally good from a theoretical point of view.
Several clues were given in a related que... | Where to cut a dendrogram?
There is no definitive answer since cluster analysis is essentially an exploratory approach; the interpretation of the resulting hierarchical structure is context-dependent and often several solutions |
2,873 | Where to cut a dendrogram? | There isn't really an answer. It's somewhere between 1 and N.
However, you can think about it from a profit perspective.
For example, in marketing one uses segmentation, which is much like clustering.
A message (an advertisement or letter, say) that is tailored for each individual will have the highest response rate. ... | Where to cut a dendrogram? | There isn't really an answer. It's somewhere between 1 and N.
However, you can think about it from a profit perspective.
For example, in marketing one uses segmentation, which is much like clustering. | Where to cut a dendrogram?
There isn't really an answer. It's somewhere between 1 and N.
However, you can think about it from a profit perspective.
For example, in marketing one uses segmentation, which is much like clustering.
A message (an advertisement or letter, say) that is tailored for each individual will have ... | Where to cut a dendrogram?
There isn't really an answer. It's somewhere between 1 and N.
However, you can think about it from a profit perspective.
For example, in marketing one uses segmentation, which is much like clustering. |
2,874 | Where to cut a dendrogram? | Perhaps one of the simplest methods would be a graphical representation in which the x-axis is the number of groups and the y-axis any evaluation metric as the distance or the similarity. In that plot you usually can observe two differentiated regions, being the x-axis value at the 'knee' of the line the 'optimal' numb... | Where to cut a dendrogram? | Perhaps one of the simplest methods would be a graphical representation in which the x-axis is the number of groups and the y-axis any evaluation metric as the distance or the similarity. In that plot | Where to cut a dendrogram?
Perhaps one of the simplest methods would be a graphical representation in which the x-axis is the number of groups and the y-axis any evaluation metric as the distance or the similarity. In that plot you usually can observe two differentiated regions, being the x-axis value at the 'knee' of ... | Where to cut a dendrogram?
Perhaps one of the simplest methods would be a graphical representation in which the x-axis is the number of groups and the y-axis any evaluation metric as the distance or the similarity. In that plot |
2,875 | Where to cut a dendrogram? | There is also "Clustergram: visualization and diagnostics for cluster analysis" (with R code)
Not really an answer, but another interesting idea for the toolbox. | Where to cut a dendrogram? | There is also "Clustergram: visualization and diagnostics for cluster analysis" (with R code)
Not really an answer, but another interesting idea for the toolbox. | Where to cut a dendrogram?
There is also "Clustergram: visualization and diagnostics for cluster analysis" (with R code)
Not really an answer, but another interesting idea for the toolbox. | Where to cut a dendrogram?
There is also "Clustergram: visualization and diagnostics for cluster analysis" (with R code)
Not really an answer, but another interesting idea for the toolbox. |
2,876 | Where to cut a dendrogram? | In hierarchical clustering the number of output partitions is not just the horizontal cuts,
but also the non horizontal cuts which decides the final clustering. Thus this can be seen as a third criterion aside the 1. distance metric and 2. Linkage criterion.
http://en.wikipedia.org/wiki/Hierarchical_clustering
The c... | Where to cut a dendrogram? | In hierarchical clustering the number of output partitions is not just the horizontal cuts,
but also the non horizontal cuts which decides the final clustering. Thus this can be seen as a third crite | Where to cut a dendrogram?
In hierarchical clustering the number of output partitions is not just the horizontal cuts,
but also the non horizontal cuts which decides the final clustering. Thus this can be seen as a third criterion aside the 1. distance metric and 2. Linkage criterion.
http://en.wikipedia.org/wiki/Hie... | Where to cut a dendrogram?
In hierarchical clustering the number of output partitions is not just the horizontal cuts,
but also the non horizontal cuts which decides the final clustering. Thus this can be seen as a third crite |
2,877 | Where to cut a dendrogram? | Some academic paper is giving a precise answer to that problem, under some separation assumptions (stability/noise resilience) on the clusters of the flat partition.
The coarse idea of the paper solution is to extract the flat partition by cutting at different levels in the dendrogram. Say you want to minimize intra-cl... | Where to cut a dendrogram? | Some academic paper is giving a precise answer to that problem, under some separation assumptions (stability/noise resilience) on the clusters of the flat partition.
The coarse idea of the paper solut | Where to cut a dendrogram?
Some academic paper is giving a precise answer to that problem, under some separation assumptions (stability/noise resilience) on the clusters of the flat partition.
The coarse idea of the paper solution is to extract the flat partition by cutting at different levels in the dendrogram. Say yo... | Where to cut a dendrogram?
Some academic paper is giving a precise answer to that problem, under some separation assumptions (stability/noise resilience) on the clusters of the flat partition.
The coarse idea of the paper solut |
2,878 | Where to cut a dendrogram? | As the other answers said, it is definitely subjective and dependent on what type of granularity you are trying to study. For a general approach, I cut this one to give me 2 clusters and 1 outlier. I would then focus on the two clusters to see if there was anything significant between them.
# Init
import pandas as p... | Where to cut a dendrogram? | As the other answers said, it is definitely subjective and dependent on what type of granularity you are trying to study. For a general approach, I cut this one to give me 2 clusters and 1 outlier. | Where to cut a dendrogram?
As the other answers said, it is definitely subjective and dependent on what type of granularity you are trying to study. For a general approach, I cut this one to give me 2 clusters and 1 outlier. I would then focus on the two clusters to see if there was anything significant between them.... | Where to cut a dendrogram?
As the other answers said, it is definitely subjective and dependent on what type of granularity you are trying to study. For a general approach, I cut this one to give me 2 clusters and 1 outlier. |
2,879 | KL divergence between two multivariate Gaussians | Starting with where you began with some slight corrections, we can write
$$
\begin{aligned}
KL &= \int \left[ \frac{1}{2} \log\frac{|\Sigma_2|}{|\Sigma_1|} - \frac{1}{2} (x-\mu_1)^T\Sigma_1^{-1}(x-\mu_1) + \frac{1}{2} (x-\mu_2)^T\Sigma_2^{-1}(x-\mu_2) \right] \times p(x) dx \\
&= \frac{1}{2} \log\frac{|\Sigma_2|}{|\Sig... | KL divergence between two multivariate Gaussians | Starting with where you began with some slight corrections, we can write
$$
\begin{aligned}
KL &= \int \left[ \frac{1}{2} \log\frac{|\Sigma_2|}{|\Sigma_1|} - \frac{1}{2} (x-\mu_1)^T\Sigma_1^{-1}(x-\mu | KL divergence between two multivariate Gaussians
Starting with where you began with some slight corrections, we can write
$$
\begin{aligned}
KL &= \int \left[ \frac{1}{2} \log\frac{|\Sigma_2|}{|\Sigma_1|} - \frac{1}{2} (x-\mu_1)^T\Sigma_1^{-1}(x-\mu_1) + \frac{1}{2} (x-\mu_2)^T\Sigma_2^{-1}(x-\mu_2) \right] \times p(x)... | KL divergence between two multivariate Gaussians
Starting with where you began with some slight corrections, we can write
$$
\begin{aligned}
KL &= \int \left[ \frac{1}{2} \log\frac{|\Sigma_2|}{|\Sigma_1|} - \frac{1}{2} (x-\mu_1)^T\Sigma_1^{-1}(x-\mu |
2,880 | Practical questions on tuning Random Forests | I'm not an authoritative figure, so consider these brief practitioner notes:
More trees is always better with diminishing returns.
Deeper trees are almost always better subject to requiring more trees for similar performance.
The above two points are directly a result of the bias-variance tradeoff. Deeper trees redu... | Practical questions on tuning Random Forests | I'm not an authoritative figure, so consider these brief practitioner notes:
More trees is always better with diminishing returns.
Deeper trees are almost always better subject to requiring more trees | Practical questions on tuning Random Forests
I'm not an authoritative figure, so consider these brief practitioner notes:
More trees is always better with diminishing returns.
Deeper trees are almost always better subject to requiring more trees for similar performance.
The above two points are directly a result of t... | Practical questions on tuning Random Forests
I'm not an authoritative figure, so consider these brief practitioner notes:
More trees is always better with diminishing returns.
Deeper trees are almost always better subject to requiring more trees |
2,881 | Practical questions on tuning Random Forests | Number of trees: the bigger the better: yes. One way to evaluate and know when to stop is to monitor your error rate while building your forest (or any other evaluation criteria you could use) and detect when it converges. You could do that on the learning set itself or, if available, on an independent test set. Also, ... | Practical questions on tuning Random Forests | Number of trees: the bigger the better: yes. One way to evaluate and know when to stop is to monitor your error rate while building your forest (or any other evaluation criteria you could use) and det | Practical questions on tuning Random Forests
Number of trees: the bigger the better: yes. One way to evaluate and know when to stop is to monitor your error rate while building your forest (or any other evaluation criteria you could use) and detect when it converges. You could do that on the learning set itself or, if ... | Practical questions on tuning Random Forests
Number of trees: the bigger the better: yes. One way to evaluate and know when to stop is to monitor your error rate while building your forest (or any other evaluation criteria you could use) and det |
2,882 | Why is sample standard deviation a biased estimator of $\sigma$? | @NRH's answer to this question gives a nice, simple proof of the biasedness of the sample standard deviation. Here I will explicitly calculate the expectation of the sample standard deviation (the original poster's second question) from a normally distributed sample, at which point the bias is clear.
The unbiased samp... | Why is sample standard deviation a biased estimator of $\sigma$? | @NRH's answer to this question gives a nice, simple proof of the biasedness of the sample standard deviation. Here I will explicitly calculate the expectation of the sample standard deviation (the ori | Why is sample standard deviation a biased estimator of $\sigma$?
@NRH's answer to this question gives a nice, simple proof of the biasedness of the sample standard deviation. Here I will explicitly calculate the expectation of the sample standard deviation (the original poster's second question) from a normally distrib... | Why is sample standard deviation a biased estimator of $\sigma$?
@NRH's answer to this question gives a nice, simple proof of the biasedness of the sample standard deviation. Here I will explicitly calculate the expectation of the sample standard deviation (the ori |
2,883 | Why is sample standard deviation a biased estimator of $\sigma$? | You don't need normality. All you need is that
$$s^2 = \frac{1}{n-1} \sum_{i=1}^n(x_i - \bar{x})^2$$
is an unbiased estimator of the variance $\sigma^2$. Then use that the square root function is strictly concave such that (by a strong form of Jensen's inequality)
$$E(\sqrt{s^2}) < \sqrt{E(s^2)} = \sigma$$
unless the ... | Why is sample standard deviation a biased estimator of $\sigma$? | You don't need normality. All you need is that
$$s^2 = \frac{1}{n-1} \sum_{i=1}^n(x_i - \bar{x})^2$$
is an unbiased estimator of the variance $\sigma^2$. Then use that the square root function is str | Why is sample standard deviation a biased estimator of $\sigma$?
You don't need normality. All you need is that
$$s^2 = \frac{1}{n-1} \sum_{i=1}^n(x_i - \bar{x})^2$$
is an unbiased estimator of the variance $\sigma^2$. Then use that the square root function is strictly concave such that (by a strong form of Jensen's i... | Why is sample standard deviation a biased estimator of $\sigma$?
You don't need normality. All you need is that
$$s^2 = \frac{1}{n-1} \sum_{i=1}^n(x_i - \bar{x})^2$$
is an unbiased estimator of the variance $\sigma^2$. Then use that the square root function is str |
2,884 | Why is sample standard deviation a biased estimator of $\sigma$? | Complementing NRH's answer, if someone is teaching this to a group of students who haven't studied Jensen's inequality yet, one way to go is to define the sample standard deviation
$$
S_n = \sqrt{\sum_{i=1}^n\frac{(X_i-\bar{X}_n)^2}{n-1}} ,
$$
suppose that $S_n$ is non degenerate (therefore, $\mathrm{Var}[S_n]\ne0$)... | Why is sample standard deviation a biased estimator of $\sigma$? | Complementing NRH's answer, if someone is teaching this to a group of students who haven't studied Jensen's inequality yet, one way to go is to define the sample standard deviation
$$
S_n = \sqrt{\s | Why is sample standard deviation a biased estimator of $\sigma$?
Complementing NRH's answer, if someone is teaching this to a group of students who haven't studied Jensen's inequality yet, one way to go is to define the sample standard deviation
$$
S_n = \sqrt{\sum_{i=1}^n\frac{(X_i-\bar{X}_n)^2}{n-1}} ,
$$
suppose ... | Why is sample standard deviation a biased estimator of $\sigma$?
Complementing NRH's answer, if someone is teaching this to a group of students who haven't studied Jensen's inequality yet, one way to go is to define the sample standard deviation
$$
S_n = \sqrt{\s |
2,885 | Why is sample standard deviation a biased estimator of $\sigma$? | This is a more general result without assuming of Normal distribution. The proof goes along the lines of this paper by David E. Giles.
First, we consider Taylor's expanding $g(x) = \sqrt{x}$ about $x=\sigma^2$,
we have
$$
g(x) = \sigma + \frac{1}{2 \sigma}(x-\sigma^2) - \frac{1}{8 \sigma^3}(x-\sigma^2)^2 + R(x),
$$
wh... | Why is sample standard deviation a biased estimator of $\sigma$? | This is a more general result without assuming of Normal distribution. The proof goes along the lines of this paper by David E. Giles.
First, we consider Taylor's expanding $g(x) = \sqrt{x}$ about $x= | Why is sample standard deviation a biased estimator of $\sigma$?
This is a more general result without assuming of Normal distribution. The proof goes along the lines of this paper by David E. Giles.
First, we consider Taylor's expanding $g(x) = \sqrt{x}$ about $x=\sigma^2$,
we have
$$
g(x) = \sigma + \frac{1}{2 \sigma... | Why is sample standard deviation a biased estimator of $\sigma$?
This is a more general result without assuming of Normal distribution. The proof goes along the lines of this paper by David E. Giles.
First, we consider Taylor's expanding $g(x) = \sqrt{x}$ about $x= |
2,886 | What are some of the most common misconceptions about linear regression? | False premise: A $\boldsymbol{\hat{\beta} \approx 0}$ means that there is no strong relationship between DV and IV.Non-linear functional relationships abound, and yet data produced by many such relationships would often produce nearly zero slopes if one assumes the relationship must be linear, or even approximately lin... | What are some of the most common misconceptions about linear regression? | False premise: A $\boldsymbol{\hat{\beta} \approx 0}$ means that there is no strong relationship between DV and IV.Non-linear functional relationships abound, and yet data produced by many such relati | What are some of the most common misconceptions about linear regression?
False premise: A $\boldsymbol{\hat{\beta} \approx 0}$ means that there is no strong relationship between DV and IV.Non-linear functional relationships abound, and yet data produced by many such relationships would often produce nearly zero slopes ... | What are some of the most common misconceptions about linear regression?
False premise: A $\boldsymbol{\hat{\beta} \approx 0}$ means that there is no strong relationship between DV and IV.Non-linear functional relationships abound, and yet data produced by many such relati |
2,887 | What are some of the most common misconceptions about linear regression? | It's very common to assume that only $y$ data are subject to measurement error (or at least, that this is the only error that we shall consider). But this ignores the possibility - and consequences - of error in the $x$ measurements. This might be particularly acute in observational studies where the $x$ variables are ... | What are some of the most common misconceptions about linear regression? | It's very common to assume that only $y$ data are subject to measurement error (or at least, that this is the only error that we shall consider). But this ignores the possibility - and consequences - | What are some of the most common misconceptions about linear regression?
It's very common to assume that only $y$ data are subject to measurement error (or at least, that this is the only error that we shall consider). But this ignores the possibility - and consequences - of error in the $x$ measurements. This might be... | What are some of the most common misconceptions about linear regression?
It's very common to assume that only $y$ data are subject to measurement error (or at least, that this is the only error that we shall consider). But this ignores the possibility - and consequences - |
2,888 | What are some of the most common misconceptions about linear regression? | There are some standard misunderstandings that apply in this context as well as other statistical contexts: e.g., the meaning of $p$-values, incorrectly inferring causality, etc.
A couple of misunderstandings that I think are specific to multiple regression are:
Thinking that the variable with the larger estimated... | What are some of the most common misconceptions about linear regression? | There are some standard misunderstandings that apply in this context as well as other statistical contexts: e.g., the meaning of $p$-values, incorrectly inferring causality, etc.
A couple of misunde | What are some of the most common misconceptions about linear regression?
There are some standard misunderstandings that apply in this context as well as other statistical contexts: e.g., the meaning of $p$-values, incorrectly inferring causality, etc.
A couple of misunderstandings that I think are specific to multipl... | What are some of the most common misconceptions about linear regression?
There are some standard misunderstandings that apply in this context as well as other statistical contexts: e.g., the meaning of $p$-values, incorrectly inferring causality, etc.
A couple of misunde |
2,889 | What are some of the most common misconceptions about linear regression? | I'd say the first one you list is probably the most common -- and perhaps the most widely taught that way -- of the things that are plainly seen to be wrong, but here are some others that are less clear in some situations (whether they really apply) but may impact even more analyses, and perhaps more seriously. These a... | What are some of the most common misconceptions about linear regression? | I'd say the first one you list is probably the most common -- and perhaps the most widely taught that way -- of the things that are plainly seen to be wrong, but here are some others that are less cle | What are some of the most common misconceptions about linear regression?
I'd say the first one you list is probably the most common -- and perhaps the most widely taught that way -- of the things that are plainly seen to be wrong, but here are some others that are less clear in some situations (whether they really appl... | What are some of the most common misconceptions about linear regression?
I'd say the first one you list is probably the most common -- and perhaps the most widely taught that way -- of the things that are plainly seen to be wrong, but here are some others that are less cle |
2,890 | What are some of the most common misconceptions about linear regression? | I probably wouldn't call these misconceptions, but maybe common points of confusion/hang-ups and, in some cases, issues that researchers may not be aware of.
Multicollinearity (including the case of more variables than data points)
Heteroskedasticity
Whether values of the independent variables are subject to noise
How... | What are some of the most common misconceptions about linear regression? | I probably wouldn't call these misconceptions, but maybe common points of confusion/hang-ups and, in some cases, issues that researchers may not be aware of.
Multicollinearity (including the case of | What are some of the most common misconceptions about linear regression?
I probably wouldn't call these misconceptions, but maybe common points of confusion/hang-ups and, in some cases, issues that researchers may not be aware of.
Multicollinearity (including the case of more variables than data points)
Heteroskedasti... | What are some of the most common misconceptions about linear regression?
I probably wouldn't call these misconceptions, but maybe common points of confusion/hang-ups and, in some cases, issues that researchers may not be aware of.
Multicollinearity (including the case of |
2,891 | What are some of the most common misconceptions about linear regression? | In my experience, students frequently adopt the view the that squared errors (or OLS regression) are an inherently appropriate, accurate, and overall good thing to use, or are even without alternative.
I have frequently seen OLS advertised along with remarks that it "gives greater weight to more extreme/deviant observa... | What are some of the most common misconceptions about linear regression? | In my experience, students frequently adopt the view the that squared errors (or OLS regression) are an inherently appropriate, accurate, and overall good thing to use, or are even without alternative | What are some of the most common misconceptions about linear regression?
In my experience, students frequently adopt the view the that squared errors (or OLS regression) are an inherently appropriate, accurate, and overall good thing to use, or are even without alternative.
I have frequently seen OLS advertised along w... | What are some of the most common misconceptions about linear regression?
In my experience, students frequently adopt the view the that squared errors (or OLS regression) are an inherently appropriate, accurate, and overall good thing to use, or are even without alternative |
2,892 | What are some of the most common misconceptions about linear regression? | Another common misconception is that the error term (or disturbance in econometrics parlance) and the residuals are the same thing.
The error term is a random variable in the true model or data generating process, and is often assumed to follow a certain distribution, whereas the residuals are the deviations of the obs... | What are some of the most common misconceptions about linear regression? | Another common misconception is that the error term (or disturbance in econometrics parlance) and the residuals are the same thing.
The error term is a random variable in the true model or data genera | What are some of the most common misconceptions about linear regression?
Another common misconception is that the error term (or disturbance in econometrics parlance) and the residuals are the same thing.
The error term is a random variable in the true model or data generating process, and is often assumed to follow a ... | What are some of the most common misconceptions about linear regression?
Another common misconception is that the error term (or disturbance in econometrics parlance) and the residuals are the same thing.
The error term is a random variable in the true model or data genera |
2,893 | What are some of the most common misconceptions about linear regression? | The most common misconception I encounter is that linear regression assumes normality of errors. It doesn't. Normality is useful in connection with some aspects of linear regression e.g. small sample properties such as confidence limits of coefficients. Even for these things there are asymptotic values available for ... | What are some of the most common misconceptions about linear regression? | The most common misconception I encounter is that linear regression assumes normality of errors. It doesn't. Normality is useful in connection with some aspects of linear regression e.g. small sample | What are some of the most common misconceptions about linear regression?
The most common misconception I encounter is that linear regression assumes normality of errors. It doesn't. Normality is useful in connection with some aspects of linear regression e.g. small sample properties such as confidence limits of coeffi... | What are some of the most common misconceptions about linear regression?
The most common misconception I encounter is that linear regression assumes normality of errors. It doesn't. Normality is useful in connection with some aspects of linear regression e.g. small sample |
2,894 | What are some of the most common misconceptions about linear regression? | The one I've often seen is a misconception on applicability of linear regression in certain use cases, in practice.
For example, let us say that the variable that we are interested in is count of something (example: visitors on website) or ratio of something (example: conversion rates). In such cases, the variable can ... | What are some of the most common misconceptions about linear regression? | The one I've often seen is a misconception on applicability of linear regression in certain use cases, in practice.
For example, let us say that the variable that we are interested in is count of some | What are some of the most common misconceptions about linear regression?
The one I've often seen is a misconception on applicability of linear regression in certain use cases, in practice.
For example, let us say that the variable that we are interested in is count of something (example: visitors on website) or ratio o... | What are some of the most common misconceptions about linear regression?
The one I've often seen is a misconception on applicability of linear regression in certain use cases, in practice.
For example, let us say that the variable that we are interested in is count of some |
2,895 | What are some of the most common misconceptions about linear regression? | An error that I made is to assume a symmetry of X and Y in the OLS.
For instance, if I assume a linear relation
$$ Y = a \, X + b$$
with a and b given by my software using OLS, then I believe that assuming X as a function of Y will give using OLS the coefficients:
$$ X = \frac{1}{a} \, Y - \frac{b}{a}$$
that is wrong.
... | What are some of the most common misconceptions about linear regression? | An error that I made is to assume a symmetry of X and Y in the OLS.
For instance, if I assume a linear relation
$$ Y = a \, X + b$$
with a and b given by my software using OLS, then I believe that ass | What are some of the most common misconceptions about linear regression?
An error that I made is to assume a symmetry of X and Y in the OLS.
For instance, if I assume a linear relation
$$ Y = a \, X + b$$
with a and b given by my software using OLS, then I believe that assuming X as a function of Y will give using OLS ... | What are some of the most common misconceptions about linear regression?
An error that I made is to assume a symmetry of X and Y in the OLS.
For instance, if I assume a linear relation
$$ Y = a \, X + b$$
with a and b given by my software using OLS, then I believe that ass |
2,896 | What are some of the most common misconceptions about linear regression? | Here is one I think is frequently overlooked by researchers:
Variable interaction: researchers often look at isolated betas of individual predictors, and often don't even specify interaction terms. But in real world things interact. Without proper specification of all possible interaction terms, you don't know how you... | What are some of the most common misconceptions about linear regression? | Here is one I think is frequently overlooked by researchers:
Variable interaction: researchers often look at isolated betas of individual predictors, and often don't even specify interaction terms. B | What are some of the most common misconceptions about linear regression?
Here is one I think is frequently overlooked by researchers:
Variable interaction: researchers often look at isolated betas of individual predictors, and often don't even specify interaction terms. But in real world things interact. Without prope... | What are some of the most common misconceptions about linear regression?
Here is one I think is frequently overlooked by researchers:
Variable interaction: researchers often look at isolated betas of individual predictors, and often don't even specify interaction terms. B |
2,897 | What are some of the most common misconceptions about linear regression? | Another common misconception is that the estimates (fitted values) are not invariant to transformations, e.g.
$$f(\hat{y}_i) \neq \widehat{f(y_i)}$$ in general, where $\hat{y}_i = \vec{x}_i ^T \hat{\beta}$, the fitted regression value based on your estimated regression coefficients.
If this is what you want for monot... | What are some of the most common misconceptions about linear regression? | Another common misconception is that the estimates (fitted values) are not invariant to transformations, e.g.
$$f(\hat{y}_i) \neq \widehat{f(y_i)}$$ in general, where $\hat{y}_i = \vec{x}_i ^T \hat{\ | What are some of the most common misconceptions about linear regression?
Another common misconception is that the estimates (fitted values) are not invariant to transformations, e.g.
$$f(\hat{y}_i) \neq \widehat{f(y_i)}$$ in general, where $\hat{y}_i = \vec{x}_i ^T \hat{\beta}$, the fitted regression value based on yo... | What are some of the most common misconceptions about linear regression?
Another common misconception is that the estimates (fitted values) are not invariant to transformations, e.g.
$$f(\hat{y}_i) \neq \widehat{f(y_i)}$$ in general, where $\hat{y}_i = \vec{x}_i ^T \hat{\ |
2,898 | Is standardization needed before fitting logistic regression? | Standardization isn't required for logistic regression. The main goal of standardizing features is to help convergence of the technique used for optimization. For example, if you use Newton-Raphson to maximize the likelihood, standardizing the features makes the convergence faster. Otherwise, you can run your logistic ... | Is standardization needed before fitting logistic regression? | Standardization isn't required for logistic regression. The main goal of standardizing features is to help convergence of the technique used for optimization. For example, if you use Newton-Raphson to | Is standardization needed before fitting logistic regression?
Standardization isn't required for logistic regression. The main goal of standardizing features is to help convergence of the technique used for optimization. For example, if you use Newton-Raphson to maximize the likelihood, standardizing the features makes... | Is standardization needed before fitting logistic regression?
Standardization isn't required for logistic regression. The main goal of standardizing features is to help convergence of the technique used for optimization. For example, if you use Newton-Raphson to |
2,899 | Is standardization needed before fitting logistic regression? | If you use logistic regression with LASSO or ridge regression (as Weka Logistic class does) you should. As Hastie,Tibshirani and Friedman points out (page 82 of the pdf or at page 63 of the book):
The ridge solutions are not equivariant under scaling of the inputs, and
so one normally standardizes the inputs befo... | Is standardization needed before fitting logistic regression? | If you use logistic regression with LASSO or ridge regression (as Weka Logistic class does) you should. As Hastie,Tibshirani and Friedman points out (page 82 of the pdf or at page 63 of the book):
| Is standardization needed before fitting logistic regression?
If you use logistic regression with LASSO or ridge regression (as Weka Logistic class does) you should. As Hastie,Tibshirani and Friedman points out (page 82 of the pdf or at page 63 of the book):
The ridge solutions are not equivariant under scaling of ... | Is standardization needed before fitting logistic regression?
If you use logistic regression with LASSO or ridge regression (as Weka Logistic class does) you should. As Hastie,Tibshirani and Friedman points out (page 82 of the pdf or at page 63 of the book):
|
2,900 | Is standardization needed before fitting logistic regression? | @Aymen is right, you don't need to normalize your data for logistic regression. (For more general information, it may help to read through this CV thread: When should you center your data & when should you standardize?; you might also note that your transformation is more commonly called 'normalizing', see: How to ver... | Is standardization needed before fitting logistic regression? | @Aymen is right, you don't need to normalize your data for logistic regression. (For more general information, it may help to read through this CV thread: When should you center your data & when shou | Is standardization needed before fitting logistic regression?
@Aymen is right, you don't need to normalize your data for logistic regression. (For more general information, it may help to read through this CV thread: When should you center your data & when should you standardize?; you might also note that your transfo... | Is standardization needed before fitting logistic regression?
@Aymen is right, you don't need to normalize your data for logistic regression. (For more general information, it may help to read through this CV thread: When should you center your data & when shou |
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