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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2,001 | What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall? | In the context of binary classification:
Accuracy - How many instances did the model label correctly?
Recall - How often was the model able to find positives?
Precision - How believable the model is when it says an instance is a positive? | What is the best way to remember the difference between sensitivity, specificity, precision, accurac | In the context of binary classification:
Accuracy - How many instances did the model label correctly?
Recall - How often was the model able to find positives?
Precision - How believable the model is w | What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall?
In the context of binary classification:
Accuracy - How many instances did the model label correctly?
Recall - How often was the model able to find positives?
Precision - How believable the model is when i... | What is the best way to remember the difference between sensitivity, specificity, precision, accurac
In the context of binary classification:
Accuracy - How many instances did the model label correctly?
Recall - How often was the model able to find positives?
Precision - How believable the model is w |
2,002 | What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall? | The following article helps me a lot
https://medium.com/swlh/how-to-remember-all-these-classification-concepts-forever-761c065be33
accuracy: Double-A rule
precision: Triple-P rule | What is the best way to remember the difference between sensitivity, specificity, precision, accurac | The following article helps me a lot
https://medium.com/swlh/how-to-remember-all-these-classification-concepts-forever-761c065be33
accuracy: Double-A rule
precision: Triple-P rule | What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall?
The following article helps me a lot
https://medium.com/swlh/how-to-remember-all-these-classification-concepts-forever-761c065be33
accuracy: Double-A rule
precision: Triple-P rule | What is the best way to remember the difference between sensitivity, specificity, precision, accurac
The following article helps me a lot
https://medium.com/swlh/how-to-remember-all-these-classification-concepts-forever-761c065be33
accuracy: Double-A rule
precision: Triple-P rule |
2,003 | What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall? | I created an interactive confusion table to help me understand the difference between these terms: http://zyxue.github.io/2018/05/15/on-the-p-value.html#interactive-confusion-table. I post the link here in case someone may find it helpful, too. | What is the best way to remember the difference between sensitivity, specificity, precision, accurac | I created an interactive confusion table to help me understand the difference between these terms: http://zyxue.github.io/2018/05/15/on-the-p-value.html#interactive-confusion-table. I post the link he | What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall?
I created an interactive confusion table to help me understand the difference between these terms: http://zyxue.github.io/2018/05/15/on-the-p-value.html#interactive-confusion-table. I post the link here in... | What is the best way to remember the difference between sensitivity, specificity, precision, accurac
I created an interactive confusion table to help me understand the difference between these terms: http://zyxue.github.io/2018/05/15/on-the-p-value.html#interactive-confusion-table. I post the link he |
2,004 | What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall? | I had a similar problem and came across Andrew Ng's slide, which I found helpful, although there are good answers here as well.
As highlighted by other answers the key is remembering the confusion matrix.
Positives are on the first row and negatives are on the bottom row.
Andrew Ng Explanation:
For both precision and ... | What is the best way to remember the difference between sensitivity, specificity, precision, accurac | I had a similar problem and came across Andrew Ng's slide, which I found helpful, although there are good answers here as well.
As highlighted by other answers the key is remembering the confusion mat | What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall?
I had a similar problem and came across Andrew Ng's slide, which I found helpful, although there are good answers here as well.
As highlighted by other answers the key is remembering the confusion matrix.
... | What is the best way to remember the difference between sensitivity, specificity, precision, accurac
I had a similar problem and came across Andrew Ng's slide, which I found helpful, although there are good answers here as well.
As highlighted by other answers the key is remembering the confusion mat |
2,005 | What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall? | I'll try and explain how I remember what recall is.
Definition:
Recall = True positives/All real world positives. OR
Recall = True positives/True Positives and False Negatives.
Imagine an automobile company that wants to recall some of its cars for a manufacturing defect (hard to imagine, right?). This company obviousl... | What is the best way to remember the difference between sensitivity, specificity, precision, accurac | I'll try and explain how I remember what recall is.
Definition:
Recall = True positives/All real world positives. OR
Recall = True positives/True Positives and False Negatives.
Imagine an automobile c | What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall?
I'll try and explain how I remember what recall is.
Definition:
Recall = True positives/All real world positives. OR
Recall = True positives/True Positives and False Negatives.
Imagine an automobile compan... | What is the best way to remember the difference between sensitivity, specificity, precision, accurac
I'll try and explain how I remember what recall is.
Definition:
Recall = True positives/All real world positives. OR
Recall = True positives/True Positives and False Negatives.
Imagine an automobile c |
2,006 | What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall? | I use the word TARP to remember the difference between accuracy and precision.
TARP: True=Accuracy, Relative=Precision.
Accuracy measures how close a measurement is to the TRUE value, as the standard/accepted value is the TRUTH.
Precision measures how close measurements are RELATIVE to each other, or how low the spread... | What is the best way to remember the difference between sensitivity, specificity, precision, accurac | I use the word TARP to remember the difference between accuracy and precision.
TARP: True=Accuracy, Relative=Precision.
Accuracy measures how close a measurement is to the TRUE value, as the standard/ | What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall?
I use the word TARP to remember the difference between accuracy and precision.
TARP: True=Accuracy, Relative=Precision.
Accuracy measures how close a measurement is to the TRUE value, as the standard/accep... | What is the best way to remember the difference between sensitivity, specificity, precision, accurac
I use the word TARP to remember the difference between accuracy and precision.
TARP: True=Accuracy, Relative=Precision.
Accuracy measures how close a measurement is to the TRUE value, as the standard/ |
2,007 | What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall? | Specificity tackles false positive. High specificity means a low false-positive rate. (Specificity = 1 - false-positive rate)
Sensitivity tackles false negative. High sensitivity means a low false-negative rate. (Sensitivity = 1 - false-negative rate)
That's why specificity is also called true negative rate, and sensit... | What is the best way to remember the difference between sensitivity, specificity, precision, accurac | Specificity tackles false positive. High specificity means a low false-positive rate. (Specificity = 1 - false-positive rate)
Sensitivity tackles false negative. High sensitivity means a low false-neg | What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall?
Specificity tackles false positive. High specificity means a low false-positive rate. (Specificity = 1 - false-positive rate)
Sensitivity tackles false negative. High sensitivity means a low false-negative... | What is the best way to remember the difference between sensitivity, specificity, precision, accurac
Specificity tackles false positive. High specificity means a low false-positive rate. (Specificity = 1 - false-positive rate)
Sensitivity tackles false negative. High sensitivity means a low false-neg |
2,008 | What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall? | 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.
for further study see this link
https://newbiettn.git... | What is the best way to remember the difference between sensitivity, specificity, precision, accurac | 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.
| What is the best way to remember the difference between sensitivity, specificity, precision, accuracy, and recall?
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.
... | What is the best way to remember the difference between sensitivity, specificity, precision, accurac
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.
|
2,009 | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals | I said earlier that I would have a go at answering the question, so here goes...
Jaynes was being a little naughty in his paper in that a frequentist confidence interval isn't defined as an interval where we might expect the true value of the statistic to lie with high (specified) probability, so it isn't unduly surpri... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi | I said earlier that I would have a go at answering the question, so here goes...
Jaynes was being a little naughty in his paper in that a frequentist confidence interval isn't defined as an interval w | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals
I said earlier that I would have a go at answering the question, so here goes...
Jaynes was being a little naughty in his paper in that a frequentist confidence interval isn't defined as an interval where... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi
I said earlier that I would have a go at answering the question, so here goes...
Jaynes was being a little naughty in his paper in that a frequentist confidence interval isn't defined as an interval w |
2,010 | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals | This is a "fleshed out" example given in a book written by Larry Wasserman All of statistics on Page 216 (12.8 Strengths and Weaknesses of Bayesian
Inference). I basically provide what Wasserman doesn't in his book 1) an explanation for what is actually happening, rather than a throw away line; 2) the frequentist answ... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi | This is a "fleshed out" example given in a book written by Larry Wasserman All of statistics on Page 216 (12.8 Strengths and Weaknesses of Bayesian
Inference). I basically provide what Wasserman does | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals
This is a "fleshed out" example given in a book written by Larry Wasserman All of statistics on Page 216 (12.8 Strengths and Weaknesses of Bayesian
Inference). I basically provide what Wasserman doesn't ... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi
This is a "fleshed out" example given in a book written by Larry Wasserman All of statistics on Page 216 (12.8 Strengths and Weaknesses of Bayesian
Inference). I basically provide what Wasserman does |
2,011 | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals | The problem starts with your sentence :
Examples based on incorrect prior
assumptions are not acceptable as they
say nothing about the internal
consistency of the different
approaches.
Yeah well, how do you know your prior is correct?
Take the case of Bayesian inference in phylogeny. The probability of at le... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi | The problem starts with your sentence :
Examples based on incorrect prior
assumptions are not acceptable as they
say nothing about the internal
consistency of the different
approaches.
Yeah | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals
The problem starts with your sentence :
Examples based on incorrect prior
assumptions are not acceptable as they
say nothing about the internal
consistency of the different
approaches.
Yeah well... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi
The problem starts with your sentence :
Examples based on incorrect prior
assumptions are not acceptable as they
say nothing about the internal
consistency of the different
approaches.
Yeah |
2,012 | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals | Keith Winstein,
EDIT: Just to clarify, this answer describes the example given in Keith Winstein Answer on the King with the cruel statistical game. The Bayesian and Frequentist answers both use the same information, which is to ignore the information on the number of fair and unfair coins when constructing the interv... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi | Keith Winstein,
EDIT: Just to clarify, this answer describes the example given in Keith Winstein Answer on the King with the cruel statistical game. The Bayesian and Frequentist answers both use the | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals
Keith Winstein,
EDIT: Just to clarify, this answer describes the example given in Keith Winstein Answer on the King with the cruel statistical game. The Bayesian and Frequentist answers both use the same... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi
Keith Winstein,
EDIT: Just to clarify, this answer describes the example given in Keith Winstein Answer on the King with the cruel statistical game. The Bayesian and Frequentist answers both use the |
2,013 | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals | Frequentist confidence intervals bound the rate of false positives (Type I errors), and guarantee their coverage will be bounded below by the confidence parameter, even in the worst case. Bayesian credibility intervals don't.
So if the thing you care about is false positives and you need to bound them, confidence inter... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi | Frequentist confidence intervals bound the rate of false positives (Type I errors), and guarantee their coverage will be bounded below by the confidence parameter, even in the worst case. Bayesian cre | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals
Frequentist confidence intervals bound the rate of false positives (Type I errors), and guarantee their coverage will be bounded below by the confidence parameter, even in the worst case. Bayesian credibi... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi
Frequentist confidence intervals bound the rate of false positives (Type I errors), and guarantee their coverage will be bounded below by the confidence parameter, even in the worst case. Bayesian cre |
2,014 | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals | In this answer I aim to describe the difference between confidence intervals and credible intervals in an intuitive way.
I hope that this may help to understand:
why/how credible intervals are better than confidence intervals.
on which conditions the credible interval depends and when they are not always better.
Cre... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi | In this answer I aim to describe the difference between confidence intervals and credible intervals in an intuitive way.
I hope that this may help to understand:
why/how credible intervals are better | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals
In this answer I aim to describe the difference between confidence intervals and credible intervals in an intuitive way.
I hope that this may help to understand:
why/how credible intervals are better tha... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi
In this answer I aim to describe the difference between confidence intervals and credible intervals in an intuitive way.
I hope that this may help to understand:
why/how credible intervals are better |
2,015 | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals
I'm going to say "any paper in experimental science".
There's an XKCD cartoon that has made the rounds here before, which I've edited slightly:
Okay, the stick figure on the left is nuts, and the one on... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals
I'm going to say "any paper in experimental science".
There's an XKCD cartoon that | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals
Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals
I'm going to say "any paper in experimental science".
There's an XKCD cartoon that has ... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi
Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals
I'm going to say "any paper in experimental science".
There's an XKCD cartoon that |
2,016 | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals | are there examples where the frequentist confidence interval is
clearly superior to the Bayesian credible interval (as per the
challenge implicitly made by Jaynes).
Here is an example: the true $\theta$ equals $10$ but the prior on $\theta$ is concentrated about $1$. I am doing statistics for a clinical trial, and $\t... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi | are there examples where the frequentist confidence interval is
clearly superior to the Bayesian credible interval (as per the
challenge implicitly made by Jaynes).
Here is an example: the true $\the | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals
are there examples where the frequentist confidence interval is
clearly superior to the Bayesian credible interval (as per the
challenge implicitly made by Jaynes).
Here is an example: the true $\theta$ ... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi
are there examples where the frequentist confidence interval is
clearly superior to the Bayesian credible interval (as per the
challenge implicitly made by Jaynes).
Here is an example: the true $\the |
2,017 | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals | The second example in this thread compares a frequentist confidence interval to two different posterior intervals based on two different non-informative priors. Despite using all the information in the likelihood, both credible intervals can be considered inferior because: i) neither credible interval provides a long-... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi | The second example in this thread compares a frequentist confidence interval to two different posterior intervals based on two different non-informative priors. Despite using all the information in t | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confidence intervals
The second example in this thread compares a frequentist confidence interval to two different posterior intervals based on two different non-informative priors. Despite using all the information in the l... | Are there any examples where Bayesian credible intervals are obviously inferior to frequentist confi
The second example in this thread compares a frequentist confidence interval to two different posterior intervals based on two different non-informative priors. Despite using all the information in t |
2,018 | What is the reason that a likelihood function is not a pdf? | We'll start with two definitions:
A probability density function (pdf) is a non-negative function that integrates to $1$.
The likelihood is defined as the joint density of the observed data as a function of the parameter. But, as pointed out by the reference to Lehmann made by @whuber in a comment below, the likelih... | What is the reason that a likelihood function is not a pdf? | We'll start with two definitions:
A probability density function (pdf) is a non-negative function that integrates to $1$.
The likelihood is defined as the joint density of the observed data as a fu | What is the reason that a likelihood function is not a pdf?
We'll start with two definitions:
A probability density function (pdf) is a non-negative function that integrates to $1$.
The likelihood is defined as the joint density of the observed data as a function of the parameter. But, as pointed out by the referenc... | What is the reason that a likelihood function is not a pdf?
We'll start with two definitions:
A probability density function (pdf) is a non-negative function that integrates to $1$.
The likelihood is defined as the joint density of the observed data as a fu |
2,019 | What is the reason that a likelihood function is not a pdf? | Okay but the likelihood function is the joint probability density for the observed data given the parameter $θ$. As such it can be normalized to form a probability density function. So it is essentially like a pdf. | What is the reason that a likelihood function is not a pdf? | Okay but the likelihood function is the joint probability density for the observed data given the parameter $θ$. As such it can be normalized to form a probability density function. So it is essentia | What is the reason that a likelihood function is not a pdf?
Okay but the likelihood function is the joint probability density for the observed data given the parameter $θ$. As such it can be normalized to form a probability density function. So it is essentially like a pdf. | What is the reason that a likelihood function is not a pdf?
Okay but the likelihood function is the joint probability density for the observed data given the parameter $θ$. As such it can be normalized to form a probability density function. So it is essentia |
2,020 | What is the reason that a likelihood function is not a pdf? | The likelihood is defined as $\mathcal{L}(\theta; x_1,...,x_n) = f(x_1,...,x_n; \theta)$, where if f(x; θ) is a probability mass function, then the likelihood is always less than one, but if f(x; θ) is a probability density function, then the likelihood can be greater than one, since densities can be greater than one.
... | What is the reason that a likelihood function is not a pdf? | The likelihood is defined as $\mathcal{L}(\theta; x_1,...,x_n) = f(x_1,...,x_n; \theta)$, where if f(x; θ) is a probability mass function, then the likelihood is always less than one, but if f(x; θ) i | What is the reason that a likelihood function is not a pdf?
The likelihood is defined as $\mathcal{L}(\theta; x_1,...,x_n) = f(x_1,...,x_n; \theta)$, where if f(x; θ) is a probability mass function, then the likelihood is always less than one, but if f(x; θ) is a probability density function, then the likelihood can be... | What is the reason that a likelihood function is not a pdf?
The likelihood is defined as $\mathcal{L}(\theta; x_1,...,x_n) = f(x_1,...,x_n; \theta)$, where if f(x; θ) is a probability mass function, then the likelihood is always less than one, but if f(x; θ) i |
2,021 | What is the reason that a likelihood function is not a pdf? | I'm not a statistician, but my understanding is that while the likelihood function itself is not a PDF with respect to the parameter(s), it is directly related to that PDF by Bayes Rule. The likelihood function, P(X|theta), and posterior distribution, f(theta|X), are tightly linked; not "a completely different thing" a... | What is the reason that a likelihood function is not a pdf? | I'm not a statistician, but my understanding is that while the likelihood function itself is not a PDF with respect to the parameter(s), it is directly related to that PDF by Bayes Rule. The likelihoo | What is the reason that a likelihood function is not a pdf?
I'm not a statistician, but my understanding is that while the likelihood function itself is not a PDF with respect to the parameter(s), it is directly related to that PDF by Bayes Rule. The likelihood function, P(X|theta), and posterior distribution, f(theta|... | What is the reason that a likelihood function is not a pdf?
I'm not a statistician, but my understanding is that while the likelihood function itself is not a PDF with respect to the parameter(s), it is directly related to that PDF by Bayes Rule. The likelihoo |
2,022 | What is the reason that a likelihood function is not a pdf? | yoooo, lets make something clear. Likelihood is completely different from probability!, when we want to calculate the probability of for example getting x=0, when x is coming from a normal distribution with miu=0 and sigma=1, we need to define a bin, like 0.01, and integral the probability function there(pdf, in this c... | What is the reason that a likelihood function is not a pdf? | yoooo, lets make something clear. Likelihood is completely different from probability!, when we want to calculate the probability of for example getting x=0, when x is coming from a normal distributio | What is the reason that a likelihood function is not a pdf?
yoooo, lets make something clear. Likelihood is completely different from probability!, when we want to calculate the probability of for example getting x=0, when x is coming from a normal distribution with miu=0 and sigma=1, we need to define a bin, like 0.01... | What is the reason that a likelihood function is not a pdf?
yoooo, lets make something clear. Likelihood is completely different from probability!, when we want to calculate the probability of for example getting x=0, when x is coming from a normal distributio |
2,023 | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand | I would recommend Hanley’s & McNeil’s 1982 paper ‘The meaning and use of the area under a receiver operating characteristic (ROC) curve’.
Example
They have the following table of disease status and test result (corresponding to, for example, the estimated risk from a logistic model). The first number on the right is th... | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand | I would recommend Hanley’s & McNeil’s 1982 paper ‘The meaning and use of the area under a receiver operating characteristic (ROC) curve’.
Example
They have the following table of disease status and te | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand
I would recommend Hanley’s & McNeil’s 1982 paper ‘The meaning and use of the area under a receiver operating characteristic (ROC) curve’.
Example
They have the following table of disease status and test result (corresponding to, for example, the e... | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand
I would recommend Hanley’s & McNeil’s 1982 paper ‘The meaning and use of the area under a receiver operating characteristic (ROC) curve’.
Example
They have the following table of disease status and te |
2,024 | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand | Have a look at this question: Understanding ROC curve
Here's how to build a ROC curve (from that question):
Drawing ROC curve
given a data set processed by your ranking classifier
rank test examples on decreasing score
start in $(0, 0)$
for each example $x$ (in the decreasing order)
if $x$ is positive, move $1/\tex... | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand | Have a look at this question: Understanding ROC curve
Here's how to build a ROC curve (from that question):
Drawing ROC curve
given a data set processed by your ranking classifier
rank test examples | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand
Have a look at this question: Understanding ROC curve
Here's how to build a ROC curve (from that question):
Drawing ROC curve
given a data set processed by your ranking classifier
rank test examples on decreasing score
start in $(0, 0)$
for each... | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand
Have a look at this question: Understanding ROC curve
Here's how to build a ROC curve (from that question):
Drawing ROC curve
given a data set processed by your ranking classifier
rank test examples |
2,025 | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand | Karl's post has a lot of excellent information. But I have not yet seen in the past 20 years an example of an ROC curve that changed anyone's thinking in a good direction. The only value of an ROC curve in my humble opinion is that its area happens to equal a very useful concordance probability. The ROC curve itself... | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand | Karl's post has a lot of excellent information. But I have not yet seen in the past 20 years an example of an ROC curve that changed anyone's thinking in a good direction. The only value of an ROC c | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand
Karl's post has a lot of excellent information. But I have not yet seen in the past 20 years an example of an ROC curve that changed anyone's thinking in a good direction. The only value of an ROC curve in my humble opinion is that its area happ... | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand
Karl's post has a lot of excellent information. But I have not yet seen in the past 20 years an example of an ROC curve that changed anyone's thinking in a good direction. The only value of an ROC c |
2,026 | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand | Here is an alternative to the natural way of calculating AUC by simply using the trapezoidal rule to get the area under the ROC curve.
The AUC is equal to the probability that a randomly sampled positive observation has a predicted probability (of being positive) greater than a randomly sampled negative observation. Y... | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand | Here is an alternative to the natural way of calculating AUC by simply using the trapezoidal rule to get the area under the ROC curve.
The AUC is equal to the probability that a randomly sampled posi | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand
Here is an alternative to the natural way of calculating AUC by simply using the trapezoidal rule to get the area under the ROC curve.
The AUC is equal to the probability that a randomly sampled positive observation has a predicted probability (o... | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand
Here is an alternative to the natural way of calculating AUC by simply using the trapezoidal rule to get the area under the ROC curve.
The AUC is equal to the probability that a randomly sampled posi |
2,027 | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand | You have true value for observations.
Calculate posterior probability and then rank observations by this probability.
Assuming cut-off probability of $P$ and number of observations $N$:
$$\frac{\text{Sum of true ranks}-0.5PN(PN+1)}{PN(N-PN)}$$ | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand | You have true value for observations.
Calculate posterior probability and then rank observations by this probability.
Assuming cut-off probability of $P$ and number of observations $N$:
$$\frac{\t | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand
You have true value for observations.
Calculate posterior probability and then rank observations by this probability.
Assuming cut-off probability of $P$ and number of observations $N$:
$$\frac{\text{Sum of true ranks}-0.5PN(PN+1)}{PN(N-PN)}$$ | How to calculate Area Under the Curve (AUC), or the c-statistic, by hand
You have true value for observations.
Calculate posterior probability and then rank observations by this probability.
Assuming cut-off probability of $P$ and number of observations $N$:
$$\frac{\t |
2,028 | How much do we know about p-hacking "in the wild"? | EXECUTIVE SUMMARY: if "p-hacking" is to be understood broadly a la Gelman's forking paths, the answer to how prevalent it is, is that it is almost universal.
Andrew Gelman likes to write about this topic and has been posting extensively about it lately on his blog. I don't always agree with him but I like his perspect... | How much do we know about p-hacking "in the wild"? | EXECUTIVE SUMMARY: if "p-hacking" is to be understood broadly a la Gelman's forking paths, the answer to how prevalent it is, is that it is almost universal.
Andrew Gelman likes to write about this t | How much do we know about p-hacking "in the wild"?
EXECUTIVE SUMMARY: if "p-hacking" is to be understood broadly a la Gelman's forking paths, the answer to how prevalent it is, is that it is almost universal.
Andrew Gelman likes to write about this topic and has been posting extensively about it lately on his blog. I ... | How much do we know about p-hacking "in the wild"?
EXECUTIVE SUMMARY: if "p-hacking" is to be understood broadly a la Gelman's forking paths, the answer to how prevalent it is, is that it is almost universal.
Andrew Gelman likes to write about this t |
2,029 | How much do we know about p-hacking "in the wild"? | Funnel plots have been a tremendous statistical innovation that turned meta analysis on its head. Basically, a funnel plot shows the clinical and statistical significance on the same plot. Ideally, they would form a funnel shape. However, several meta-analyses have produced funnel plots that show a strong bimodal shape... | How much do we know about p-hacking "in the wild"? | Funnel plots have been a tremendous statistical innovation that turned meta analysis on its head. Basically, a funnel plot shows the clinical and statistical significance on the same plot. Ideally, th | How much do we know about p-hacking "in the wild"?
Funnel plots have been a tremendous statistical innovation that turned meta analysis on its head. Basically, a funnel plot shows the clinical and statistical significance on the same plot. Ideally, they would form a funnel shape. However, several meta-analyses have pro... | How much do we know about p-hacking "in the wild"?
Funnel plots have been a tremendous statistical innovation that turned meta analysis on its head. Basically, a funnel plot shows the clinical and statistical significance on the same plot. Ideally, th |
2,030 | Can someone explain Gibbs sampling in very simple words? [duplicate] | You are a dungeonmaster hosting Dungeons & Dragons and a player casts 'Spell of
Eldritch Chaotic Weather (SECW). You've never heard of this spell before, but it turns out it is quite involved. The player hands you a dense book and says, 'the effect of this spell is that one of the events in this book occurs.' The b... | Can someone explain Gibbs sampling in very simple words? [duplicate] | You are a dungeonmaster hosting Dungeons & Dragons and a player casts 'Spell of
Eldritch Chaotic Weather (SECW). You've never heard of this spell before, but it turns out it is quite involved. The | Can someone explain Gibbs sampling in very simple words? [duplicate]
You are a dungeonmaster hosting Dungeons & Dragons and a player casts 'Spell of
Eldritch Chaotic Weather (SECW). You've never heard of this spell before, but it turns out it is quite involved. The player hands you a dense book and says, 'the effect... | Can someone explain Gibbs sampling in very simple words? [duplicate]
You are a dungeonmaster hosting Dungeons & Dragons and a player casts 'Spell of
Eldritch Chaotic Weather (SECW). You've never heard of this spell before, but it turns out it is quite involved. The |
2,031 | Can someone explain Gibbs sampling in very simple words? [duplicate] | I find this document GIBBS SAMPLING FOR THE UNINITIATED by Resnik & Hardisty very useful for non-statistics background folks. It explains why & how to use Gibbs sampling, and has examples demonstrating the algo.
Seems I cannot comment yet.
Gibbs sampling is not a self-contained concept. It requires some prerequisite kn... | Can someone explain Gibbs sampling in very simple words? [duplicate] | I find this document GIBBS SAMPLING FOR THE UNINITIATED by Resnik & Hardisty very useful for non-statistics background folks. It explains why & how to use Gibbs sampling, and has examples demonstratin | Can someone explain Gibbs sampling in very simple words? [duplicate]
I find this document GIBBS SAMPLING FOR THE UNINITIATED by Resnik & Hardisty very useful for non-statistics background folks. It explains why & how to use Gibbs sampling, and has examples demonstrating the algo.
Seems I cannot comment yet.
Gibbs sampl... | Can someone explain Gibbs sampling in very simple words? [duplicate]
I find this document GIBBS SAMPLING FOR THE UNINITIATED by Resnik & Hardisty very useful for non-statistics background folks. It explains why & how to use Gibbs sampling, and has examples demonstratin |
2,032 | Can someone explain Gibbs sampling in very simple words? [duplicate] | From wikipedia: "The goal of Gibbs Sampling here is to approximate the distribution of $P(\mathbf{Z}|\mathbf{W};\alpha,\beta)$" Notation can be found on the wiki site or from the original paper here.
One "scan" of Gibbs sampling targeting the above distribution will give you draws from the following probability distrib... | Can someone explain Gibbs sampling in very simple words? [duplicate] | From wikipedia: "The goal of Gibbs Sampling here is to approximate the distribution of $P(\mathbf{Z}|\mathbf{W};\alpha,\beta)$" Notation can be found on the wiki site or from the original paper here.
| Can someone explain Gibbs sampling in very simple words? [duplicate]
From wikipedia: "The goal of Gibbs Sampling here is to approximate the distribution of $P(\mathbf{Z}|\mathbf{W};\alpha,\beta)$" Notation can be found on the wiki site or from the original paper here.
One "scan" of Gibbs sampling targeting the above di... | Can someone explain Gibbs sampling in very simple words? [duplicate]
From wikipedia: "The goal of Gibbs Sampling here is to approximate the distribution of $P(\mathbf{Z}|\mathbf{W};\alpha,\beta)$" Notation can be found on the wiki site or from the original paper here.
|
2,033 | What correlation makes a matrix singular and what are implications of singularity or near-singularity? | What is singular matrix?
A square matrix is singular, that is, its determinant is zero, if it contains rows or columns which are proportionally interrelated; in other words, one or more of its rows (columns) is exactly expressible as a linear combination of all or some other its rows (columns), the combination being wi... | What correlation makes a matrix singular and what are implications of singularity or near-singularit | What is singular matrix?
A square matrix is singular, that is, its determinant is zero, if it contains rows or columns which are proportionally interrelated; in other words, one or more of its rows (c | What correlation makes a matrix singular and what are implications of singularity or near-singularity?
What is singular matrix?
A square matrix is singular, that is, its determinant is zero, if it contains rows or columns which are proportionally interrelated; in other words, one or more of its rows (columns) is exactl... | What correlation makes a matrix singular and what are implications of singularity or near-singularit
What is singular matrix?
A square matrix is singular, that is, its determinant is zero, if it contains rows or columns which are proportionally interrelated; in other words, one or more of its rows (c |
2,034 | Who Are The Bayesians? | I'm going to take your questions in order:
The question is, Who are the Bayesians today?
Anybody who does Bayesian data analysis and self-identifies as "Bayesian". Just like a programmer is someone who programs and self-identifies as a "programmer". A slight difference is that for historical reasons Bayesian has ideo... | Who Are The Bayesians? | I'm going to take your questions in order:
The question is, Who are the Bayesians today?
Anybody who does Bayesian data analysis and self-identifies as "Bayesian". Just like a programmer is someone | Who Are The Bayesians?
I'm going to take your questions in order:
The question is, Who are the Bayesians today?
Anybody who does Bayesian data analysis and self-identifies as "Bayesian". Just like a programmer is someone who programs and self-identifies as a "programmer". A slight difference is that for historical re... | Who Are The Bayesians?
I'm going to take your questions in order:
The question is, Who are the Bayesians today?
Anybody who does Bayesian data analysis and self-identifies as "Bayesian". Just like a programmer is someone |
2,035 | Who Are The Bayesians? | Bayesians are people who define probabilities as a numerical representation of the plausibility of some proposition. Frequentists are people who define probabilities as representing long run frequencies. If you are only happy with one or other of these definitions then you are either a Bayesian or a frequentist. If ... | Who Are The Bayesians? | Bayesians are people who define probabilities as a numerical representation of the plausibility of some proposition. Frequentists are people who define probabilities as representing long run frequenc | Who Are The Bayesians?
Bayesians are people who define probabilities as a numerical representation of the plausibility of some proposition. Frequentists are people who define probabilities as representing long run frequencies. If you are only happy with one or other of these definitions then you are either a Bayesian... | Who Are The Bayesians?
Bayesians are people who define probabilities as a numerical representation of the plausibility of some proposition. Frequentists are people who define probabilities as representing long run frequenc |
2,036 | Who Are The Bayesians? | Andrew Gelman, for example, a professor of statistics and political science at Columbia University, is a prominent Bayesian.
I suspect the most of ISBA fellows would probably consider themselves Bayesians as well.
In general, the following research topics typically reflect a Bayesian approach. If you read papers about ... | Who Are The Bayesians? | Andrew Gelman, for example, a professor of statistics and political science at Columbia University, is a prominent Bayesian.
I suspect the most of ISBA fellows would probably consider themselves Bayes | Who Are The Bayesians?
Andrew Gelman, for example, a professor of statistics and political science at Columbia University, is a prominent Bayesian.
I suspect the most of ISBA fellows would probably consider themselves Bayesians as well.
In general, the following research topics typically reflect a Bayesian approach. If... | Who Are The Bayesians?
Andrew Gelman, for example, a professor of statistics and political science at Columbia University, is a prominent Bayesian.
I suspect the most of ISBA fellows would probably consider themselves Bayes |
2,037 | Who Are The Bayesians? | Today, we're all Bayesians, but there's a world beyond these two camps: algorithmic probability. I'm not sure what's the standard reference on this subject, but there's this beautiful paper by Kolmogorov on algorithmic complexity: A. N. Kolmogorov, Three approaches to the definition of the concept “quantity of informat... | Who Are The Bayesians? | Today, we're all Bayesians, but there's a world beyond these two camps: algorithmic probability. I'm not sure what's the standard reference on this subject, but there's this beautiful paper by Kolmogo | Who Are The Bayesians?
Today, we're all Bayesians, but there's a world beyond these two camps: algorithmic probability. I'm not sure what's the standard reference on this subject, but there's this beautiful paper by Kolmogorov on algorithmic complexity: A. N. Kolmogorov, Three approaches to the definition of the concep... | Who Are The Bayesians?
Today, we're all Bayesians, but there's a world beyond these two camps: algorithmic probability. I'm not sure what's the standard reference on this subject, but there's this beautiful paper by Kolmogo |
2,038 | Who Are The Bayesians? | The most "hard core" Bayesian that I know of is Edwin Jaynes, deceased in 1998. I'd expect further "hard core" Bayesians to be found among his pupils, especially the posthumous co-author of his main work Probability Theory: The Logic of Science, Larry Bretthorst. Other notable historic Bayesians include Harold Jeffreys... | Who Are The Bayesians? | The most "hard core" Bayesian that I know of is Edwin Jaynes, deceased in 1998. I'd expect further "hard core" Bayesians to be found among his pupils, especially the posthumous co-author of his main w | Who Are The Bayesians?
The most "hard core" Bayesian that I know of is Edwin Jaynes, deceased in 1998. I'd expect further "hard core" Bayesians to be found among his pupils, especially the posthumous co-author of his main work Probability Theory: The Logic of Science, Larry Bretthorst. Other notable historic Bayesians ... | Who Are The Bayesians?
The most "hard core" Bayesian that I know of is Edwin Jaynes, deceased in 1998. I'd expect further "hard core" Bayesians to be found among his pupils, especially the posthumous co-author of his main w |
2,039 | Who Are The Bayesians? | I don't know who the Bayesians are (although I suppose I should have a prior distribution for that), but I do know who they are not.
To quote the eminent, now departed Bayesian, D.V. Lindley, "there is no one less Bayesian than an empirical Bayesian". Empirical Bayes section of Bayesian Methods: A Social and Behavioral... | Who Are The Bayesians? | I don't know who the Bayesians are (although I suppose I should have a prior distribution for that), but I do know who they are not.
To quote the eminent, now departed Bayesian, D.V. Lindley, "there i | Who Are The Bayesians?
I don't know who the Bayesians are (although I suppose I should have a prior distribution for that), but I do know who they are not.
To quote the eminent, now departed Bayesian, D.V. Lindley, "there is no one less Bayesian than an empirical Bayesian". Empirical Bayes section of Bayesian Methods: ... | Who Are The Bayesians?
I don't know who the Bayesians are (although I suppose I should have a prior distribution for that), but I do know who they are not.
To quote the eminent, now departed Bayesian, D.V. Lindley, "there i |
2,040 | Who Are The Bayesians? | I'm probably too late to this discussion for anyone to notice this, but I think it is a shame that no-one has pointed out the fact that the most important difference between Bayesian and Frequentist approaches is that the Bayesians (mostly) use methods that respect the likelihood principle whereas Frequentists almost i... | Who Are The Bayesians? | I'm probably too late to this discussion for anyone to notice this, but I think it is a shame that no-one has pointed out the fact that the most important difference between Bayesian and Frequentist a | Who Are The Bayesians?
I'm probably too late to this discussion for anyone to notice this, but I think it is a shame that no-one has pointed out the fact that the most important difference between Bayesian and Frequentist approaches is that the Bayesians (mostly) use methods that respect the likelihood principle wherea... | Who Are The Bayesians?
I'm probably too late to this discussion for anyone to notice this, but I think it is a shame that no-one has pointed out the fact that the most important difference between Bayesian and Frequentist a |
2,041 | Who Are The Bayesians? | You may believe you're a Bayesian, but you're probably wrong... http://www.rmm-journal.de/downloads/Article_Senn.pdf
Bayesians derive the probability distribution of outcomes of interest given prior belief / prior information. To a Bayesian this distribution (and its summaries) are what most people will be interested ... | Who Are The Bayesians? | You may believe you're a Bayesian, but you're probably wrong... http://www.rmm-journal.de/downloads/Article_Senn.pdf
Bayesians derive the probability distribution of outcomes of interest given prior | Who Are The Bayesians?
You may believe you're a Bayesian, but you're probably wrong... http://www.rmm-journal.de/downloads/Article_Senn.pdf
Bayesians derive the probability distribution of outcomes of interest given prior belief / prior information. To a Bayesian this distribution (and its summaries) are what most peo... | Who Are The Bayesians?
You may believe you're a Bayesian, but you're probably wrong... http://www.rmm-journal.de/downloads/Article_Senn.pdf
Bayesians derive the probability distribution of outcomes of interest given prior |
2,042 | Who Are The Bayesians? | Just to take up your last question (so I'm not after a prize!), about a link between a Bayesian/Frequentist approach and one's epistemological position, the most interesting author I've come upon is Deborah Mayo. A good starting point is this 2010 exchange between Mayo and Andrew Gelman (who emerges here as a somewhat ... | Who Are The Bayesians? | Just to take up your last question (so I'm not after a prize!), about a link between a Bayesian/Frequentist approach and one's epistemological position, the most interesting author I've come upon is D | Who Are The Bayesians?
Just to take up your last question (so I'm not after a prize!), about a link between a Bayesian/Frequentist approach and one's epistemological position, the most interesting author I've come upon is Deborah Mayo. A good starting point is this 2010 exchange between Mayo and Andrew Gelman (who emer... | Who Are The Bayesians?
Just to take up your last question (so I'm not after a prize!), about a link between a Bayesian/Frequentist approach and one's epistemological position, the most interesting author I've come upon is D |
2,043 | Who Are The Bayesians? | A subset of all Bayesians, i.e. those Bayesians who bothered to send an email, is listed here. | Who Are The Bayesians? | A subset of all Bayesians, i.e. those Bayesians who bothered to send an email, is listed here. | Who Are The Bayesians?
A subset of all Bayesians, i.e. those Bayesians who bothered to send an email, is listed here. | Who Are The Bayesians?
A subset of all Bayesians, i.e. those Bayesians who bothered to send an email, is listed here. |
2,044 | Who Are The Bayesians? | I would call Bruno de Finetti and L. J. Savage Bayesians. They worked on its philosophical foundations. | Who Are The Bayesians? | I would call Bruno de Finetti and L. J. Savage Bayesians. They worked on its philosophical foundations. | Who Are The Bayesians?
I would call Bruno de Finetti and L. J. Savage Bayesians. They worked on its philosophical foundations. | Who Are The Bayesians?
I would call Bruno de Finetti and L. J. Savage Bayesians. They worked on its philosophical foundations. |
2,045 | Who Are The Bayesians? | For understanding the foundational debate between frequentists and Bayesians, it would be hard to find a more authoritative voice than Bradley Efron.
This topic has been a theme he has touched on numerous times in his career, but personally I found one of his older papers helpful: Controversies in the Foundations of St... | Who Are The Bayesians? | For understanding the foundational debate between frequentists and Bayesians, it would be hard to find a more authoritative voice than Bradley Efron.
This topic has been a theme he has touched on nume | Who Are The Bayesians?
For understanding the foundational debate between frequentists and Bayesians, it would be hard to find a more authoritative voice than Bradley Efron.
This topic has been a theme he has touched on numerous times in his career, but personally I found one of his older papers helpful: Controversies i... | Who Are The Bayesians?
For understanding the foundational debate between frequentists and Bayesians, it would be hard to find a more authoritative voice than Bradley Efron.
This topic has been a theme he has touched on nume |
2,046 | How to choose nlme or lme4 R library for mixed effects models? | Both packages use Lattice as the backend, but nlme has some nice features like groupedData() and lmList() that are lacking in lme4 (IMO). From a practical perspective, the two most important criteria seem, however, that
lme4 extends nlme with other link functions: in nlme, you cannot fit outcomes whose distribution is... | How to choose nlme or lme4 R library for mixed effects models? | Both packages use Lattice as the backend, but nlme has some nice features like groupedData() and lmList() that are lacking in lme4 (IMO). From a practical perspective, the two most important criteria | How to choose nlme or lme4 R library for mixed effects models?
Both packages use Lattice as the backend, but nlme has some nice features like groupedData() and lmList() that are lacking in lme4 (IMO). From a practical perspective, the two most important criteria seem, however, that
lme4 extends nlme with other link fu... | How to choose nlme or lme4 R library for mixed effects models?
Both packages use Lattice as the backend, but nlme has some nice features like groupedData() and lmList() that are lacking in lme4 (IMO). From a practical perspective, the two most important criteria |
2,047 | How to choose nlme or lme4 R library for mixed effects models? | As chl pointed out, the main difference is what kind of variance-covariance structure you can specify for the random effects. In lme4 you can specify either:
diagonal covariance structures (i.e., enforce mutually uncorrelated random effects via syntax like ~ (1 | group)+ (0 + x1 | group) + (0 + x2 | group))
or unstruc... | How to choose nlme or lme4 R library for mixed effects models? | As chl pointed out, the main difference is what kind of variance-covariance structure you can specify for the random effects. In lme4 you can specify either:
diagonal covariance structures (i.e., enf | How to choose nlme or lme4 R library for mixed effects models?
As chl pointed out, the main difference is what kind of variance-covariance structure you can specify for the random effects. In lme4 you can specify either:
diagonal covariance structures (i.e., enforce mutually uncorrelated random effects via syntax like... | How to choose nlme or lme4 R library for mixed effects models?
As chl pointed out, the main difference is what kind of variance-covariance structure you can specify for the random effects. In lme4 you can specify either:
diagonal covariance structures (i.e., enf |
2,048 | How to choose nlme or lme4 R library for mixed effects models? | Others have summarized the differences very well. My impression is that lme4 is more suited for clustered data sets especially when you need to use crossed random effects. For repeated measures designs (including many longitudinal designs) however, nlme is the tool since only nlme supports specifying a correlation stru... | How to choose nlme or lme4 R library for mixed effects models? | Others have summarized the differences very well. My impression is that lme4 is more suited for clustered data sets especially when you need to use crossed random effects. For repeated measures design | How to choose nlme or lme4 R library for mixed effects models?
Others have summarized the differences very well. My impression is that lme4 is more suited for clustered data sets especially when you need to use crossed random effects. For repeated measures designs (including many longitudinal designs) however, nlme is ... | How to choose nlme or lme4 R library for mixed effects models?
Others have summarized the differences very well. My impression is that lme4 is more suited for clustered data sets especially when you need to use crossed random effects. For repeated measures design |
2,049 | How to choose nlme or lme4 R library for mixed effects models? | There are actually a number of packages in R for fitting mixed effects models beyond lme4 and nlme. There's a nice wiki run by the R special interest group for mixed models, which has a very nice FAQ and a page comparing the different packages.
As for my opinions on actually using lme4 and nlme: I found lme4 to be gene... | How to choose nlme or lme4 R library for mixed effects models? | There are actually a number of packages in R for fitting mixed effects models beyond lme4 and nlme. There's a nice wiki run by the R special interest group for mixed models, which has a very nice FAQ | How to choose nlme or lme4 R library for mixed effects models?
There are actually a number of packages in R for fitting mixed effects models beyond lme4 and nlme. There's a nice wiki run by the R special interest group for mixed models, which has a very nice FAQ and a page comparing the different packages.
As for my op... | How to choose nlme or lme4 R library for mixed effects models?
There are actually a number of packages in R for fitting mixed effects models beyond lme4 and nlme. There's a nice wiki run by the R special interest group for mixed models, which has a very nice FAQ |
2,050 | Mutual information versus correlation | Let's consider one fundamental concept of (linear) correlation, covariance (which is Pearson's correlation coefficient "un-standardized"). For two discrete random variables $X$ and $Y$ with probability mass functions $p(x)$, $p(y)$ and joint pmf $p(x,y)$ we have
$$\operatorname{Cov}(X,Y) = E(XY) - E(X)E(Y) = \sum_{x,y}... | Mutual information versus correlation | Let's consider one fundamental concept of (linear) correlation, covariance (which is Pearson's correlation coefficient "un-standardized"). For two discrete random variables $X$ and $Y$ with probabilit | Mutual information versus correlation
Let's consider one fundamental concept of (linear) correlation, covariance (which is Pearson's correlation coefficient "un-standardized"). For two discrete random variables $X$ and $Y$ with probability mass functions $p(x)$, $p(y)$ and joint pmf $p(x,y)$ we have
$$\operatorname{Cov... | Mutual information versus correlation
Let's consider one fundamental concept of (linear) correlation, covariance (which is Pearson's correlation coefficient "un-standardized"). For two discrete random variables $X$ and $Y$ with probabilit |
2,051 | Mutual information versus correlation | Here's an example.
In these two plots the correlation coefficient is zero. But we can get high shared mutual information even when the correlation is zero.
In the first, I see that if I have a high or low value of X then I'm likely to get a high value of Y. But if the value of X is moderate then I have a low value ... | Mutual information versus correlation | Here's an example.
In these two plots the correlation coefficient is zero. But we can get high shared mutual information even when the correlation is zero.
In the first, I see that if I have a high | Mutual information versus correlation
Here's an example.
In these two plots the correlation coefficient is zero. But we can get high shared mutual information even when the correlation is zero.
In the first, I see that if I have a high or low value of X then I'm likely to get a high value of Y. But if the value of ... | Mutual information versus correlation
Here's an example.
In these two plots the correlation coefficient is zero. But we can get high shared mutual information even when the correlation is zero.
In the first, I see that if I have a high |
2,052 | Mutual information versus correlation | Mutual information is a distance between two probability distributions. Correlation is a linear distance between two random variables.
You can have a mutual information between any two probabilities defined for a set of symbols, while you cannot have a correlation between symbols that cannot naturally be mapped into a ... | Mutual information versus correlation | Mutual information is a distance between two probability distributions. Correlation is a linear distance between two random variables.
You can have a mutual information between any two probabilities d | Mutual information versus correlation
Mutual information is a distance between two probability distributions. Correlation is a linear distance between two random variables.
You can have a mutual information between any two probabilities defined for a set of symbols, while you cannot have a correlation between symbols t... | Mutual information versus correlation
Mutual information is a distance between two probability distributions. Correlation is a linear distance between two random variables.
You can have a mutual information between any two probabilities d |
2,053 | Mutual information versus correlation | Although both of them are a measure of relationship between features, the MI is more general than correlation coefficient (CE) sine the CE is only able to takes into account linear relationships but the MI can also handle non-linear relationships. | Mutual information versus correlation | Although both of them are a measure of relationship between features, the MI is more general than correlation coefficient (CE) sine the CE is only able to takes into account linear relationships but t | Mutual information versus correlation
Although both of them are a measure of relationship between features, the MI is more general than correlation coefficient (CE) sine the CE is only able to takes into account linear relationships but the MI can also handle non-linear relationships. | Mutual information versus correlation
Although both of them are a measure of relationship between features, the MI is more general than correlation coefficient (CE) sine the CE is only able to takes into account linear relationships but t |
2,054 | Mutual information versus correlation | Mutual Information (MI) uses the concept entropy to specify how much common certainty are there in two data samples $X$ and $Y$ with distribution functions $p_{x}(x)$ and $p_y(y)$. Considering this interpretation of MI: $$I(X:Y) = H(X) + H(Y) - H(X,Y)$$ we see that the last part says about the dependency of variables. ... | Mutual information versus correlation | Mutual Information (MI) uses the concept entropy to specify how much common certainty are there in two data samples $X$ and $Y$ with distribution functions $p_{x}(x)$ and $p_y(y)$. Considering this in | Mutual information versus correlation
Mutual Information (MI) uses the concept entropy to specify how much common certainty are there in two data samples $X$ and $Y$ with distribution functions $p_{x}(x)$ and $p_y(y)$. Considering this interpretation of MI: $$I(X:Y) = H(X) + H(Y) - H(X,Y)$$ we see that the last part sa... | Mutual information versus correlation
Mutual Information (MI) uses the concept entropy to specify how much common certainty are there in two data samples $X$ and $Y$ with distribution functions $p_{x}(x)$ and $p_y(y)$. Considering this in |
2,055 | Mutual information versus correlation | Note that Correlation(Pearson, Spearman or Kendell) takes values in $[-1,1]$ while Mutual Information takes value in $\mathbb{R^*}$. This makes a big difference: a correlation score is a stronger description of the association between the two RVs than the mutual information. On the other hand, although mutual informat... | Mutual information versus correlation | Note that Correlation(Pearson, Spearman or Kendell) takes values in $[-1,1]$ while Mutual Information takes value in $\mathbb{R^*}$. This makes a big difference: a correlation score is a stronger desc | Mutual information versus correlation
Note that Correlation(Pearson, Spearman or Kendell) takes values in $[-1,1]$ while Mutual Information takes value in $\mathbb{R^*}$. This makes a big difference: a correlation score is a stronger description of the association between the two RVs than the mutual information. On th... | Mutual information versus correlation
Note that Correlation(Pearson, Spearman or Kendell) takes values in $[-1,1]$ while Mutual Information takes value in $\mathbb{R^*}$. This makes a big difference: a correlation score is a stronger desc |
2,056 | Calculating the parameters of a Beta distribution using the mean and variance | I set$$\mu=\frac{\alpha}{\alpha+\beta}$$and$$\sigma^2=\frac{\alpha\beta}{(\alpha+\beta)^2(\alpha+\beta+1)}$$and solved for $\alpha$ and $\beta$. My results show that$$\alpha=\left(\frac{1-\mu}{\sigma^2}-\frac{1}{\mu}\right)\mu^2$$and$$\beta=\alpha\left(\frac{1}{\mu}-1\right)$$
I've written up some R code to estimate th... | Calculating the parameters of a Beta distribution using the mean and variance | I set$$\mu=\frac{\alpha}{\alpha+\beta}$$and$$\sigma^2=\frac{\alpha\beta}{(\alpha+\beta)^2(\alpha+\beta+1)}$$and solved for $\alpha$ and $\beta$. My results show that$$\alpha=\left(\frac{1-\mu}{\sigma^ | Calculating the parameters of a Beta distribution using the mean and variance
I set$$\mu=\frac{\alpha}{\alpha+\beta}$$and$$\sigma^2=\frac{\alpha\beta}{(\alpha+\beta)^2(\alpha+\beta+1)}$$and solved for $\alpha$ and $\beta$. My results show that$$\alpha=\left(\frac{1-\mu}{\sigma^2}-\frac{1}{\mu}\right)\mu^2$$and$$\beta=\... | Calculating the parameters of a Beta distribution using the mean and variance
I set$$\mu=\frac{\alpha}{\alpha+\beta}$$and$$\sigma^2=\frac{\alpha\beta}{(\alpha+\beta)^2(\alpha+\beta+1)}$$and solved for $\alpha$ and $\beta$. My results show that$$\alpha=\left(\frac{1-\mu}{\sigma^ |
2,057 | Calculating the parameters of a Beta distribution using the mean and variance | Here's a generic way to solve these types of problems, using Maple instead of R. This works for other distributions as well:
with(Statistics):
eq1 := mu = Mean(BetaDistribution(alpha, beta)):
eq2 := sigma^2 = Variance(BetaDistribution(alpha, beta)):
solve([eq1, eq2], [alpha, beta]);
which leads to the solution
$$
\be... | Calculating the parameters of a Beta distribution using the mean and variance | Here's a generic way to solve these types of problems, using Maple instead of R. This works for other distributions as well:
with(Statistics):
eq1 := mu = Mean(BetaDistribution(alpha, beta)):
eq2 := s | Calculating the parameters of a Beta distribution using the mean and variance
Here's a generic way to solve these types of problems, using Maple instead of R. This works for other distributions as well:
with(Statistics):
eq1 := mu = Mean(BetaDistribution(alpha, beta)):
eq2 := sigma^2 = Variance(BetaDistribution(alpha, ... | Calculating the parameters of a Beta distribution using the mean and variance
Here's a generic way to solve these types of problems, using Maple instead of R. This works for other distributions as well:
with(Statistics):
eq1 := mu = Mean(BetaDistribution(alpha, beta)):
eq2 := s |
2,058 | Calculating the parameters of a Beta distribution using the mean and variance | In R, the beta distribution with parameters $\textbf{shape1} = a$ and $\textbf{shape2} = b$ has density
$f(x) = \frac{\Gamma(a+b)}{\Gamma(a) \Gamma(b)} x^{a-1}(1-x)^{b-1}$,
for $a > 0$, $b >0$, and $0 < x < 1$.
In R, you can compute it by
dbeta(x, shape1=a, shape2=b)
In that parametrisation, the mean is $E(X) = \frac... | Calculating the parameters of a Beta distribution using the mean and variance | In R, the beta distribution with parameters $\textbf{shape1} = a$ and $\textbf{shape2} = b$ has density
$f(x) = \frac{\Gamma(a+b)}{\Gamma(a) \Gamma(b)} x^{a-1}(1-x)^{b-1}$,
for $a > 0$, $b >0$, and $0 | Calculating the parameters of a Beta distribution using the mean and variance
In R, the beta distribution with parameters $\textbf{shape1} = a$ and $\textbf{shape2} = b$ has density
$f(x) = \frac{\Gamma(a+b)}{\Gamma(a) \Gamma(b)} x^{a-1}(1-x)^{b-1}$,
for $a > 0$, $b >0$, and $0 < x < 1$.
In R, you can compute it by
db... | Calculating the parameters of a Beta distribution using the mean and variance
In R, the beta distribution with parameters $\textbf{shape1} = a$ and $\textbf{shape2} = b$ has density
$f(x) = \frac{\Gamma(a+b)}{\Gamma(a) \Gamma(b)} x^{a-1}(1-x)^{b-1}$,
for $a > 0$, $b >0$, and $0 |
2,059 | Calculating the parameters of a Beta distribution using the mean and variance | On Wikipedia for example, you can find the following formulas for mean and variance of a beta distribution given alpha and beta:
$$
\mu=\frac{\alpha}{\alpha+\beta}
$$
and
$$
\sigma^2=\frac{\alpha\beta}{(\alpha+\beta)^2(\alpha+\beta+1)}
$$
Inverting these ( fill out $\beta=\alpha(\frac{1}{\mu}-1)$ in the bottom equa... | Calculating the parameters of a Beta distribution using the mean and variance | On Wikipedia for example, you can find the following formulas for mean and variance of a beta distribution given alpha and beta:
$$
\mu=\frac{\alpha}{\alpha+\beta}
$$
and
$$
\sigma^2=\frac{\alpha\b | Calculating the parameters of a Beta distribution using the mean and variance
On Wikipedia for example, you can find the following formulas for mean and variance of a beta distribution given alpha and beta:
$$
\mu=\frac{\alpha}{\alpha+\beta}
$$
and
$$
\sigma^2=\frac{\alpha\beta}{(\alpha+\beta)^2(\alpha+\beta+1)}
$$... | Calculating the parameters of a Beta distribution using the mean and variance
On Wikipedia for example, you can find the following formulas for mean and variance of a beta distribution given alpha and beta:
$$
\mu=\frac{\alpha}{\alpha+\beta}
$$
and
$$
\sigma^2=\frac{\alpha\b |
2,060 | Calculating the parameters of a Beta distribution using the mean and variance | For a generalized Beta distribution defined on the interval $[a,b]$, you have the relations:
$$\mu=\frac{a\beta+b\alpha}{\alpha+\beta},\quad\sigma^{2}=\frac{\alpha\beta\left(b-a\right)^{2}}{\left(\alpha+\beta\right)^{2}\left(1+\alpha+\beta\right)}$$
which can be inverted to give:
$$\alpha=\lambda\frac{\mu-a}{b-a},\quad... | Calculating the parameters of a Beta distribution using the mean and variance | For a generalized Beta distribution defined on the interval $[a,b]$, you have the relations:
$$\mu=\frac{a\beta+b\alpha}{\alpha+\beta},\quad\sigma^{2}=\frac{\alpha\beta\left(b-a\right)^{2}}{\left(\alp | Calculating the parameters of a Beta distribution using the mean and variance
For a generalized Beta distribution defined on the interval $[a,b]$, you have the relations:
$$\mu=\frac{a\beta+b\alpha}{\alpha+\beta},\quad\sigma^{2}=\frac{\alpha\beta\left(b-a\right)^{2}}{\left(\alpha+\beta\right)^{2}\left(1+\alpha+\beta\ri... | Calculating the parameters of a Beta distribution using the mean and variance
For a generalized Beta distribution defined on the interval $[a,b]$, you have the relations:
$$\mu=\frac{a\beta+b\alpha}{\alpha+\beta},\quad\sigma^{2}=\frac{\alpha\beta\left(b-a\right)^{2}}{\left(\alp |
2,061 | Calculating the parameters of a Beta distribution using the mean and variance | Solve the $\mu$ equation for either $\alpha$ or $\beta$, solving for $\beta$, you get $$\beta=\frac{\alpha(1-\mu)}{\mu}$$ Then plug this into the second equation, and solve for $\alpha$. So you get $$\sigma^2=\frac{\frac{\alpha^2(1-\mu)}{\mu}}{(\alpha+\frac{\alpha(1-\mu)}{\mu})^2(\alpha+\frac{\alpha(1-\mu)}{\mu}+1)}$... | Calculating the parameters of a Beta distribution using the mean and variance | Solve the $\mu$ equation for either $\alpha$ or $\beta$, solving for $\beta$, you get $$\beta=\frac{\alpha(1-\mu)}{\mu}$$ Then plug this into the second equation, and solve for $\alpha$. So you get | Calculating the parameters of a Beta distribution using the mean and variance
Solve the $\mu$ equation for either $\alpha$ or $\beta$, solving for $\beta$, you get $$\beta=\frac{\alpha(1-\mu)}{\mu}$$ Then plug this into the second equation, and solve for $\alpha$. So you get $$\sigma^2=\frac{\frac{\alpha^2(1-\mu)}{\m... | Calculating the parameters of a Beta distribution using the mean and variance
Solve the $\mu$ equation for either $\alpha$ or $\beta$, solving for $\beta$, you get $$\beta=\frac{\alpha(1-\mu)}{\mu}$$ Then plug this into the second equation, and solve for $\alpha$. So you get |
2,062 | Calculating the parameters of a Beta distribution using the mean and variance | I was looking for python, but stumbled upon this. So this would be useful for others like me.
Here is a python code to estimate beta parameters (according to the equations given above):
# estimate parameters of beta dist.
def getAlphaBeta(mu, sigma):
alpha = mu**2 * ((1 - mu) / sigma**2 - 1 / mu)
beta = alpha ... | Calculating the parameters of a Beta distribution using the mean and variance | I was looking for python, but stumbled upon this. So this would be useful for others like me.
Here is a python code to estimate beta parameters (according to the equations given above):
# estimate par | Calculating the parameters of a Beta distribution using the mean and variance
I was looking for python, but stumbled upon this. So this would be useful for others like me.
Here is a python code to estimate beta parameters (according to the equations given above):
# estimate parameters of beta dist.
def getAlphaBeta(mu,... | Calculating the parameters of a Beta distribution using the mean and variance
I was looking for python, but stumbled upon this. So this would be useful for others like me.
Here is a python code to estimate beta parameters (according to the equations given above):
# estimate par |
2,063 | What is a complete list of the usual assumptions for linear regression? | The answer depends heavily on how do you define complete and usual. Suppose we write linear regression model in the following way:$
\newcommand{\x}{\mathbf{x}}
\newcommand{\bet}{\boldsymbol\beta}
\DeclareMathOperator{\E}{\mathbb{E}}
\DeclareMathOperator{\Var}{Var}
\DeclareMathOperator{\Cov}{Cov}
\DeclareMathOperator{\T... | What is a complete list of the usual assumptions for linear regression? | The answer depends heavily on how do you define complete and usual. Suppose we write linear regression model in the following way:$
\newcommand{\x}{\mathbf{x}}
\newcommand{\bet}{\boldsymbol\beta}
\Dec | What is a complete list of the usual assumptions for linear regression?
The answer depends heavily on how do you define complete and usual. Suppose we write linear regression model in the following way:$
\newcommand{\x}{\mathbf{x}}
\newcommand{\bet}{\boldsymbol\beta}
\DeclareMathOperator{\E}{\mathbb{E}}
\DeclareMathOp... | What is a complete list of the usual assumptions for linear regression?
The answer depends heavily on how do you define complete and usual. Suppose we write linear regression model in the following way:$
\newcommand{\x}{\mathbf{x}}
\newcommand{\bet}{\boldsymbol\beta}
\Dec |
2,064 | What is a complete list of the usual assumptions for linear regression? | There are a number of good answers here. It occurs to me that there is one assumption that has not been stated however (at least not explicitly). Specifically, a regression model assumes that $\mathbf X$ (the values of your explanatory / predictor variables) is fixed and known, and that all of the uncertainty in the ... | What is a complete list of the usual assumptions for linear regression? | There are a number of good answers here. It occurs to me that there is one assumption that has not been stated however (at least not explicitly). Specifically, a regression model assumes that $\math | What is a complete list of the usual assumptions for linear regression?
There are a number of good answers here. It occurs to me that there is one assumption that has not been stated however (at least not explicitly). Specifically, a regression model assumes that $\mathbf X$ (the values of your explanatory / predict... | What is a complete list of the usual assumptions for linear regression?
There are a number of good answers here. It occurs to me that there is one assumption that has not been stated however (at least not explicitly). Specifically, a regression model assumes that $\math |
2,065 | What is a complete list of the usual assumptions for linear regression? | The following diagrams show which assumptions are required to get which implications in the finite and asymptotic scenarios.
Linear Regression Assumptions: Key Points
Generally the assumptions can be broken down into what we need for our coefficient estimators
to be right on average--unbiased--or right with infinite... | What is a complete list of the usual assumptions for linear regression? | The following diagrams show which assumptions are required to get which implications in the finite and asymptotic scenarios.
Linear Regression Assumptions: Key Points
Generally the assumptions can b | What is a complete list of the usual assumptions for linear regression?
The following diagrams show which assumptions are required to get which implications in the finite and asymptotic scenarios.
Linear Regression Assumptions: Key Points
Generally the assumptions can be broken down into what we need for our coeffic... | What is a complete list of the usual assumptions for linear regression?
The following diagrams show which assumptions are required to get which implications in the finite and asymptotic scenarios.
Linear Regression Assumptions: Key Points
Generally the assumptions can b |
2,066 | What is a complete list of the usual assumptions for linear regression? | The assumptions of the classical linear regression model include:
Linear Parameter and correct model specification
Full Rank of the X Matrix
Explanatory Variables must be exogenous
Independent and Identically Distributed Error Terms
Normal Distributed Error Terms in Population
Although the answers here provide al... | What is a complete list of the usual assumptions for linear regression? | The assumptions of the classical linear regression model include:
Linear Parameter and correct model specification
Full Rank of the X Matrix
Explanatory Variables must be exogenous
Independent and | What is a complete list of the usual assumptions for linear regression?
The assumptions of the classical linear regression model include:
Linear Parameter and correct model specification
Full Rank of the X Matrix
Explanatory Variables must be exogenous
Independent and Identically Distributed Error Terms
Normal Di... | What is a complete list of the usual assumptions for linear regression?
The assumptions of the classical linear regression model include:
Linear Parameter and correct model specification
Full Rank of the X Matrix
Explanatory Variables must be exogenous
Independent and |
2,067 | What is a complete list of the usual assumptions for linear regression? | Different assumptions can be used to justify OLS
In some situations, an author tests the residuals for normality.
But in other situations, the residuals aren't normal and the author uses OLS anyway!
You'll see texts saying that homoscedasticity is an assumption.
But you see researchers using OLS when homoscedasti... | What is a complete list of the usual assumptions for linear regression? | Different assumptions can be used to justify OLS
In some situations, an author tests the residuals for normality.
But in other situations, the residuals aren't normal and the author uses OLS anyway | What is a complete list of the usual assumptions for linear regression?
Different assumptions can be used to justify OLS
In some situations, an author tests the residuals for normality.
But in other situations, the residuals aren't normal and the author uses OLS anyway!
You'll see texts saying that homoscedasticit... | What is a complete list of the usual assumptions for linear regression?
Different assumptions can be used to justify OLS
In some situations, an author tests the residuals for normality.
But in other situations, the residuals aren't normal and the author uses OLS anyway |
2,068 | What is a complete list of the usual assumptions for linear regression? | It's all about what you want to do with your model. Imagine if your errors were positively skewed/non-normal. If you wanted to make a prediction interval, you could do better than using the t-distribution. If your variance is smaller at smaller predicted values, again, you'd be making a prediction interval that's too... | What is a complete list of the usual assumptions for linear regression? | It's all about what you want to do with your model. Imagine if your errors were positively skewed/non-normal. If you wanted to make a prediction interval, you could do better than using the t-distri | What is a complete list of the usual assumptions for linear regression?
It's all about what you want to do with your model. Imagine if your errors were positively skewed/non-normal. If you wanted to make a prediction interval, you could do better than using the t-distribution. If your variance is smaller at smaller ... | What is a complete list of the usual assumptions for linear regression?
It's all about what you want to do with your model. Imagine if your errors were positively skewed/non-normal. If you wanted to make a prediction interval, you could do better than using the t-distri |
2,069 | What is a complete list of the usual assumptions for linear regression? | The least squares regression coefficient provides a way to summarize the first order trend in any kind of data. @mpiktas answer is a thorough treatment of the conditions under which least squares is increasingly optimal. I'd like to go the other way and show the most general case when least squares works. Let's see th... | What is a complete list of the usual assumptions for linear regression? | The least squares regression coefficient provides a way to summarize the first order trend in any kind of data. @mpiktas answer is a thorough treatment of the conditions under which least squares is | What is a complete list of the usual assumptions for linear regression?
The least squares regression coefficient provides a way to summarize the first order trend in any kind of data. @mpiktas answer is a thorough treatment of the conditions under which least squares is increasingly optimal. I'd like to go the other ... | What is a complete list of the usual assumptions for linear regression?
The least squares regression coefficient provides a way to summarize the first order trend in any kind of data. @mpiktas answer is a thorough treatment of the conditions under which least squares is |
2,070 | What is a complete list of the usual assumptions for linear regression? | There is no such a thing as a single list of assumptions, there will be at least 2: one for fixed and one for random design matrix. Plus you may want to look at the assumptions for time series regressions (see p.13)
The case when the design matrix $X$ is fixed could be the most common one, and its assumptions are ofte... | What is a complete list of the usual assumptions for linear regression? | There is no such a thing as a single list of assumptions, there will be at least 2: one for fixed and one for random design matrix. Plus you may want to look at the assumptions for time series regress | What is a complete list of the usual assumptions for linear regression?
There is no such a thing as a single list of assumptions, there will be at least 2: one for fixed and one for random design matrix. Plus you may want to look at the assumptions for time series regressions (see p.13)
The case when the design matri... | What is a complete list of the usual assumptions for linear regression?
There is no such a thing as a single list of assumptions, there will be at least 2: one for fixed and one for random design matrix. Plus you may want to look at the assumptions for time series regress |
2,071 | What is a complete list of the usual assumptions for linear regression? | The assumption of linearity is that the model is linear in the parameters. It is fine to have a regression model with quadratic or higher order effects as long as the power function of the independent variable is part of a linear additive model. If the model does not contain higher order terms when it should, then th... | What is a complete list of the usual assumptions for linear regression? | The assumption of linearity is that the model is linear in the parameters. It is fine to have a regression model with quadratic or higher order effects as long as the power function of the independen | What is a complete list of the usual assumptions for linear regression?
The assumption of linearity is that the model is linear in the parameters. It is fine to have a regression model with quadratic or higher order effects as long as the power function of the independent variable is part of a linear additive model. ... | What is a complete list of the usual assumptions for linear regression?
The assumption of linearity is that the model is linear in the parameters. It is fine to have a regression model with quadratic or higher order effects as long as the power function of the independen |
2,072 | What is a complete list of the usual assumptions for linear regression? | The following are the assumptions of Linear Regression analysis.
Correct specification. The linear functional form is correctly specified.
Strict exogeneity. The errors in the regression should have conditional mean zero.
No multicollinearity. The regressors in X must all be linearly independent.
Homoscedasticity which... | What is a complete list of the usual assumptions for linear regression? | The following are the assumptions of Linear Regression analysis.
Correct specification. The linear functional form is correctly specified.
Strict exogeneity. The errors in the regression should have c | What is a complete list of the usual assumptions for linear regression?
The following are the assumptions of Linear Regression analysis.
Correct specification. The linear functional form is correctly specified.
Strict exogeneity. The errors in the regression should have conditional mean zero.
No multicollinearity. The... | What is a complete list of the usual assumptions for linear regression?
The following are the assumptions of Linear Regression analysis.
Correct specification. The linear functional form is correctly specified.
Strict exogeneity. The errors in the regression should have c |
2,073 | What is the difference between Cross-entropy and KL divergence? | You will need some conditions to claim the equivalence between minimizing cross entropy and minimizing KL divergence. I will put your question under the context of classification problems using cross entropy as loss functions.
Let us first recall that entropy is used to measure the uncertainty of a system, which is def... | What is the difference between Cross-entropy and KL divergence? | You will need some conditions to claim the equivalence between minimizing cross entropy and minimizing KL divergence. I will put your question under the context of classification problems using cross | What is the difference between Cross-entropy and KL divergence?
You will need some conditions to claim the equivalence between minimizing cross entropy and minimizing KL divergence. I will put your question under the context of classification problems using cross entropy as loss functions.
Let us first recall that entr... | What is the difference between Cross-entropy and KL divergence?
You will need some conditions to claim the equivalence between minimizing cross entropy and minimizing KL divergence. I will put your question under the context of classification problems using cross |
2,074 | What is the difference between Cross-entropy and KL divergence? | I suppose it is because the models usually work with the samples packed in mini-batches. For KL divergence and Cross-Entropy, their relation can be written as
$$H(q, p) = D_{KL}(p, q)+H(p) = -\sum_i{p_i\log(q_i)}$$
so have $$D_{KL}(p, q) = H(q, p) - H(p)$$
From the equation, we could see that KL divergence can depart i... | What is the difference between Cross-entropy and KL divergence? | I suppose it is because the models usually work with the samples packed in mini-batches. For KL divergence and Cross-Entropy, their relation can be written as
$$H(q, p) = D_{KL}(p, q)+H(p) = -\sum_i{p | What is the difference between Cross-entropy and KL divergence?
I suppose it is because the models usually work with the samples packed in mini-batches. For KL divergence and Cross-Entropy, their relation can be written as
$$H(q, p) = D_{KL}(p, q)+H(p) = -\sum_i{p_i\log(q_i)}$$
so have $$D_{KL}(p, q) = H(q, p) - H(p)$$... | What is the difference between Cross-entropy and KL divergence?
I suppose it is because the models usually work with the samples packed in mini-batches. For KL divergence and Cross-Entropy, their relation can be written as
$$H(q, p) = D_{KL}(p, q)+H(p) = -\sum_i{p |
2,075 | What is the difference between Cross-entropy and KL divergence? | This is how I think about it:
$$
D_{KL}(p(y_i | x_i) \:||\: q(y_i | x_i, \theta)) = H(p(y_i | x_i, \theta), q(y_i | x_i, \theta)) - H(p(y_i | x_i, \theta)) \tag{1}\label{eq:kl}
$$
where $p$ and $q$ are two probability distributions. In machine learning, we typically know $p$, which is the distribution of the target. Fo... | What is the difference between Cross-entropy and KL divergence? | This is how I think about it:
$$
D_{KL}(p(y_i | x_i) \:||\: q(y_i | x_i, \theta)) = H(p(y_i | x_i, \theta), q(y_i | x_i, \theta)) - H(p(y_i | x_i, \theta)) \tag{1}\label{eq:kl}
$$
where $p$ and $q$ ar | What is the difference between Cross-entropy and KL divergence?
This is how I think about it:
$$
D_{KL}(p(y_i | x_i) \:||\: q(y_i | x_i, \theta)) = H(p(y_i | x_i, \theta), q(y_i | x_i, \theta)) - H(p(y_i | x_i, \theta)) \tag{1}\label{eq:kl}
$$
where $p$ and $q$ are two probability distributions. In machine learning, we... | What is the difference between Cross-entropy and KL divergence?
This is how I think about it:
$$
D_{KL}(p(y_i | x_i) \:||\: q(y_i | x_i, \theta)) = H(p(y_i | x_i, \theta), q(y_i | x_i, \theta)) - H(p(y_i | x_i, \theta)) \tag{1}\label{eq:kl}
$$
where $p$ and $q$ ar |
2,076 | What is the difference between Cross-entropy and KL divergence? | @zewen's answer can be misleading as he claims that in mini-batch training, CE can be more robust than KL. In most of standard mini-batch training, we use gradient-based approach, and the gradient of $H(p)$ with respect to $q$ (which is a function of our model parameter) would be zero. So in these cases, CE and KL as a... | What is the difference between Cross-entropy and KL divergence? | @zewen's answer can be misleading as he claims that in mini-batch training, CE can be more robust than KL. In most of standard mini-batch training, we use gradient-based approach, and the gradient of | What is the difference between Cross-entropy and KL divergence?
@zewen's answer can be misleading as he claims that in mini-batch training, CE can be more robust than KL. In most of standard mini-batch training, we use gradient-based approach, and the gradient of $H(p)$ with respect to $q$ (which is a function of our m... | What is the difference between Cross-entropy and KL divergence?
@zewen's answer can be misleading as he claims that in mini-batch training, CE can be more robust than KL. In most of standard mini-batch training, we use gradient-based approach, and the gradient of |
2,077 | What is the difference between Cross-entropy and KL divergence? | Some answers are already provided, while I would like to point out regarding the question itself
measure the distance between two probability distributions
that neither of cross-entropy and KL divergence measures the distance between two distributions-- instead they measure the difference of two distributions [1]. It... | What is the difference between Cross-entropy and KL divergence? | Some answers are already provided, while I would like to point out regarding the question itself
measure the distance between two probability distributions
that neither of cross-entropy and KL diver | What is the difference between Cross-entropy and KL divergence?
Some answers are already provided, while I would like to point out regarding the question itself
measure the distance between two probability distributions
that neither of cross-entropy and KL divergence measures the distance between two distributions-- ... | What is the difference between Cross-entropy and KL divergence?
Some answers are already provided, while I would like to point out regarding the question itself
measure the distance between two probability distributions
that neither of cross-entropy and KL diver |
2,078 | What is the difference between Cross-entropy and KL divergence? | Minimizing an importance sampling estimate of the KL divergence is equivalent to minimizing the cross entropy loss of these importance samples. | What is the difference between Cross-entropy and KL divergence? | Minimizing an importance sampling estimate of the KL divergence is equivalent to minimizing the cross entropy loss of these importance samples. | What is the difference between Cross-entropy and KL divergence?
Minimizing an importance sampling estimate of the KL divergence is equivalent to minimizing the cross entropy loss of these importance samples. | What is the difference between Cross-entropy and KL divergence?
Minimizing an importance sampling estimate of the KL divergence is equivalent to minimizing the cross entropy loss of these importance samples. |
2,079 | How to 'sum' a standard deviation? | Short answer: You average the variances; then you can take square root to get the average standard deviation.
Example
Month MWh StdDev Variance
========== ===== ====== ========
January 927 333 110889
February 1234 250 62500
March 1032 301 90601
April 8... | How to 'sum' a standard deviation? | Short answer: You average the variances; then you can take square root to get the average standard deviation.
Example
Month MWh StdDev Variance
========== ===== ====== ========
January | How to 'sum' a standard deviation?
Short answer: You average the variances; then you can take square root to get the average standard deviation.
Example
Month MWh StdDev Variance
========== ===== ====== ========
January 927 333 110889
February 1234 250 62500
March 1032... | How to 'sum' a standard deviation?
Short answer: You average the variances; then you can take square root to get the average standard deviation.
Example
Month MWh StdDev Variance
========== ===== ====== ========
January |
2,080 | How to 'sum' a standard deviation? | This is an old question but the answer accepted is incorrect or at least incomplete.
The user wants to calculate the standard deviation over 12-month data where the mean and standard deviation are already calculated over each month.
Assuming that the number of samples in each month is the same, then it is possible to c... | How to 'sum' a standard deviation? | This is an old question but the answer accepted is incorrect or at least incomplete.
The user wants to calculate the standard deviation over 12-month data where the mean and standard deviation are alr | How to 'sum' a standard deviation?
This is an old question but the answer accepted is incorrect or at least incomplete.
The user wants to calculate the standard deviation over 12-month data where the mean and standard deviation are already calculated over each month.
Assuming that the number of samples in each month is... | How to 'sum' a standard deviation?
This is an old question but the answer accepted is incorrect or at least incomplete.
The user wants to calculate the standard deviation over 12-month data where the mean and standard deviation are alr |
2,081 | How to 'sum' a standard deviation? | TL;DR
Given several days, and for each day we are given its Average, Sample StdDev and number of Samples, denoted as:
$$
\mu_d,\ \sigma_d,\ N_d
$$
We would like to compute the Average and Sample StdDev across all days.
Average is simply a weighted average:
$$
\mu = \frac{\sum{\mu_dN_d}}{\sum{N_d}} = \frac{\sum{\mu_dN_d... | How to 'sum' a standard deviation? | TL;DR
Given several days, and for each day we are given its Average, Sample StdDev and number of Samples, denoted as:
$$
\mu_d,\ \sigma_d,\ N_d
$$
We would like to compute the Average and Sample StdDe | How to 'sum' a standard deviation?
TL;DR
Given several days, and for each day we are given its Average, Sample StdDev and number of Samples, denoted as:
$$
\mu_d,\ \sigma_d,\ N_d
$$
We would like to compute the Average and Sample StdDev across all days.
Average is simply a weighted average:
$$
\mu = \frac{\sum{\mu_dN_d... | How to 'sum' a standard deviation?
TL;DR
Given several days, and for each day we are given its Average, Sample StdDev and number of Samples, denoted as:
$$
\mu_d,\ \sigma_d,\ N_d
$$
We would like to compute the Average and Sample StdDe |
2,082 | How to 'sum' a standard deviation? | I'd like to stress again the incorrectness in part of the accepted answer. The wording of the question lead to confusion.
The question have Average and StdDev of each month, but it's unclear what kind of subset is used. Is it the average of 1 wind turbine of the whole farm or the daily average of the whole farm? If it'... | How to 'sum' a standard deviation? | I'd like to stress again the incorrectness in part of the accepted answer. The wording of the question lead to confusion.
The question have Average and StdDev of each month, but it's unclear what kind | How to 'sum' a standard deviation?
I'd like to stress again the incorrectness in part of the accepted answer. The wording of the question lead to confusion.
The question have Average and StdDev of each month, but it's unclear what kind of subset is used. Is it the average of 1 wind turbine of the whole farm or the dail... | How to 'sum' a standard deviation?
I'd like to stress again the incorrectness in part of the accepted answer. The wording of the question lead to confusion.
The question have Average and StdDev of each month, but it's unclear what kind |
2,083 | How to 'sum' a standard deviation? | I believe what you may be really interested in though is the standard error rather than the standard deviation.
The standard error of the mean (SEM) is the standard deviation of the sample-mean's estimate of a population mean, and that will give you a measure how how good your yearly MWh estimate is.
It's very easy to ... | How to 'sum' a standard deviation? | I believe what you may be really interested in though is the standard error rather than the standard deviation.
The standard error of the mean (SEM) is the standard deviation of the sample-mean's esti | How to 'sum' a standard deviation?
I believe what you may be really interested in though is the standard error rather than the standard deviation.
The standard error of the mean (SEM) is the standard deviation of the sample-mean's estimate of a population mean, and that will give you a measure how how good your yearly ... | How to 'sum' a standard deviation?
I believe what you may be really interested in though is the standard error rather than the standard deviation.
The standard error of the mean (SEM) is the standard deviation of the sample-mean's esti |
2,084 | How to 'sum' a standard deviation? | If you know the number of samples used for the calculation of the monthly mean and standard deviation, you can use the "batch extension" by Chan et al. of Welford's algorithm to combine the variances (squares of standard deviations) and means of data subsets. The algorithm is numerically robust and exact.
See this Wiki... | How to 'sum' a standard deviation? | If you know the number of samples used for the calculation of the monthly mean and standard deviation, you can use the "batch extension" by Chan et al. of Welford's algorithm to combine the variances | How to 'sum' a standard deviation?
If you know the number of samples used for the calculation of the monthly mean and standard deviation, you can use the "batch extension" by Chan et al. of Welford's algorithm to combine the variances (squares of standard deviations) and means of data subsets. The algorithm is numerica... | How to 'sum' a standard deviation?
If you know the number of samples used for the calculation of the monthly mean and standard deviation, you can use the "batch extension" by Chan et al. of Welford's algorithm to combine the variances |
2,085 | When to use regularization methods for regression? | Short answer: Whenever you are facing one of these situations:
large number of variables or low ratio of no. observations to no. variables (including the $n\ll p$ case),
high collinearity,
seeking for a sparse solution (i.e., embed feature selection when estimating model parameters), or
accounting for variables gro... | When to use regularization methods for regression? | Short answer: Whenever you are facing one of these situations:
large number of variables or low ratio of no. observations to no. variables (including the $n\ll p$ case),
high collinearity,
seeking | When to use regularization methods for regression?
Short answer: Whenever you are facing one of these situations:
large number of variables or low ratio of no. observations to no. variables (including the $n\ll p$ case),
high collinearity,
seeking for a sparse solution (i.e., embed feature selection when estimating ... | When to use regularization methods for regression?
Short answer: Whenever you are facing one of these situations:
large number of variables or low ratio of no. observations to no. variables (including the $n\ll p$ case),
high collinearity,
seeking |
2,086 | When to use regularization methods for regression? | A theoretical justification for the use of ridge regression is that its solution is the posterior mean given a normal prior on the coefficients. That is, if you care about squared error and you believe in a normal prior, the ridge estimates are optimal.
Similarly, the lasso estimate is the posterior mode under a doubl... | When to use regularization methods for regression? | A theoretical justification for the use of ridge regression is that its solution is the posterior mean given a normal prior on the coefficients. That is, if you care about squared error and you belie | When to use regularization methods for regression?
A theoretical justification for the use of ridge regression is that its solution is the posterior mean given a normal prior on the coefficients. That is, if you care about squared error and you believe in a normal prior, the ridge estimates are optimal.
Similarly, the... | When to use regularization methods for regression?
A theoretical justification for the use of ridge regression is that its solution is the posterior mean given a normal prior on the coefficients. That is, if you care about squared error and you belie |
2,087 | How to plot ROC curves in multiclass classification? | It seems you are looking for multi-class ROC analysis, which is a kind of multi-objective optimization covered in a tutorial at ICML'04. As in several multi-class problem, the idea is generally to carry out pairwise comparison (one class vs. all other classes, one class vs. another class, see (1) or the Elements of Sta... | How to plot ROC curves in multiclass classification? | It seems you are looking for multi-class ROC analysis, which is a kind of multi-objective optimization covered in a tutorial at ICML'04. As in several multi-class problem, the idea is generally to car | How to plot ROC curves in multiclass classification?
It seems you are looking for multi-class ROC analysis, which is a kind of multi-objective optimization covered in a tutorial at ICML'04. As in several multi-class problem, the idea is generally to carry out pairwise comparison (one class vs. all other classes, one cl... | How to plot ROC curves in multiclass classification?
It seems you are looking for multi-class ROC analysis, which is a kind of multi-objective optimization covered in a tutorial at ICML'04. As in several multi-class problem, the idea is generally to car |
2,088 | How to plot ROC curves in multiclass classification? | I recently found this pROC package in R which plots a multiclass ROC using the technique specified by Hand and Till (2001). You can use the multiclass.roc function. | How to plot ROC curves in multiclass classification? | I recently found this pROC package in R which plots a multiclass ROC using the technique specified by Hand and Till (2001). You can use the multiclass.roc function. | How to plot ROC curves in multiclass classification?
I recently found this pROC package in R which plots a multiclass ROC using the technique specified by Hand and Till (2001). You can use the multiclass.roc function. | How to plot ROC curves in multiclass classification?
I recently found this pROC package in R which plots a multiclass ROC using the technique specified by Hand and Till (2001). You can use the multiclass.roc function. |
2,089 | How to plot ROC curves in multiclass classification? | You need to specify your classifier to act as one-vs-rest, and then you can plot individual ROC curves. There's a handy library for doing it without much work in python called yellowbrick.
Check out the docs with a minimal reproducible example.
The result looks like this (source) | How to plot ROC curves in multiclass classification? | You need to specify your classifier to act as one-vs-rest, and then you can plot individual ROC curves. There's a handy library for doing it without much work in python called yellowbrick.
Check out t | How to plot ROC curves in multiclass classification?
You need to specify your classifier to act as one-vs-rest, and then you can plot individual ROC curves. There's a handy library for doing it without much work in python called yellowbrick.
Check out the docs with a minimal reproducible example.
The result looks like ... | How to plot ROC curves in multiclass classification?
You need to specify your classifier to act as one-vs-rest, and then you can plot individual ROC curves. There's a handy library for doing it without much work in python called yellowbrick.
Check out t |
2,090 | How to plot ROC curves in multiclass classification? | The answers here are pretty complete, but I still would like to add my 5 cents. In this question you can find an example of R code for producing ROC Curves using One-Vs-All Approach and the ROCR R library.
This is the plot from that answer: | How to plot ROC curves in multiclass classification? | The answers here are pretty complete, but I still would like to add my 5 cents. In this question you can find an example of R code for producing ROC Curves using One-Vs-All Approach and the ROCR R lib | How to plot ROC curves in multiclass classification?
The answers here are pretty complete, but I still would like to add my 5 cents. In this question you can find an example of R code for producing ROC Curves using One-Vs-All Approach and the ROCR R library.
This is the plot from that answer: | How to plot ROC curves in multiclass classification?
The answers here are pretty complete, but I still would like to add my 5 cents. In this question you can find an example of R code for producing ROC Curves using One-Vs-All Approach and the ROCR R lib |
2,091 | How to plot ROC curves in multiclass classification? | While the math is beyond me this general review article has some references you will likely be interested in, and has a brief description of multi-class ROC graphs.
An introduction to ROC analysis by Tom Fawcett
Pattern Recognition Letters
Volume 27, Issue 8, June 2006, Pages 861-874
Link to pdf as provided by gd047- t... | How to plot ROC curves in multiclass classification? | While the math is beyond me this general review article has some references you will likely be interested in, and has a brief description of multi-class ROC graphs.
An introduction to ROC analysis by | How to plot ROC curves in multiclass classification?
While the math is beyond me this general review article has some references you will likely be interested in, and has a brief description of multi-class ROC graphs.
An introduction to ROC analysis by Tom Fawcett
Pattern Recognition Letters
Volume 27, Issue 8, June 20... | How to plot ROC curves in multiclass classification?
While the math is beyond me this general review article has some references you will likely be interested in, and has a brief description of multi-class ROC graphs.
An introduction to ROC analysis by |
2,092 | How to compute precision/recall for multiclass-multilabel classification? | For multi-label classification you have two ways to go
First consider the following.
$n$ is the number of examples.
$Y_i$ is the ground truth label assignment of the $i^{th}$ example..
$x_i$ is the $i^{th}$ example.
$h(x_i)$ is the predicted labels for the $i^{th}$ example.
Example based
The metrics are computed in a... | How to compute precision/recall for multiclass-multilabel classification? | For multi-label classification you have two ways to go
First consider the following.
$n$ is the number of examples.
$Y_i$ is the ground truth label assignment of the $i^{th}$ example..
$x_i$ is the $ | How to compute precision/recall for multiclass-multilabel classification?
For multi-label classification you have two ways to go
First consider the following.
$n$ is the number of examples.
$Y_i$ is the ground truth label assignment of the $i^{th}$ example..
$x_i$ is the $i^{th}$ example.
$h(x_i)$ is the predicted lab... | How to compute precision/recall for multiclass-multilabel classification?
For multi-label classification you have two ways to go
First consider the following.
$n$ is the number of examples.
$Y_i$ is the ground truth label assignment of the $i^{th}$ example..
$x_i$ is the $ |
2,093 | How to compute precision/recall for multiclass-multilabel classification? | Another popular tool for measuring classifier performance is ROC/AUC ; this one too has a multi-class / multi-label extension : see [Hand 2001]
[Hand 2001]: A simple generalization of the area under the ROC curve to multiple class classification problems | How to compute precision/recall for multiclass-multilabel classification? | Another popular tool for measuring classifier performance is ROC/AUC ; this one too has a multi-class / multi-label extension : see [Hand 2001]
[Hand 2001]: A simple generalization of the area under t | How to compute precision/recall for multiclass-multilabel classification?
Another popular tool for measuring classifier performance is ROC/AUC ; this one too has a multi-class / multi-label extension : see [Hand 2001]
[Hand 2001]: A simple generalization of the area under the ROC curve to multiple class classification ... | How to compute precision/recall for multiclass-multilabel classification?
Another popular tool for measuring classifier performance is ROC/AUC ; this one too has a multi-class / multi-label extension : see [Hand 2001]
[Hand 2001]: A simple generalization of the area under t |
2,094 | How to compute precision/recall for multiclass-multilabel classification? | Here is some discuss of coursera forum thread about confusion matrix and multi-class precision/recall measurement.
The basic idea is to compute all precision and recall of all the classes, then average them to get a single real number measurement.
Confusion matrix make it easy to compute precision and recall of a class... | How to compute precision/recall for multiclass-multilabel classification? | Here is some discuss of coursera forum thread about confusion matrix and multi-class precision/recall measurement.
The basic idea is to compute all precision and recall of all the classes, then averag | How to compute precision/recall for multiclass-multilabel classification?
Here is some discuss of coursera forum thread about confusion matrix and multi-class precision/recall measurement.
The basic idea is to compute all precision and recall of all the classes, then average them to get a single real number measurement... | How to compute precision/recall for multiclass-multilabel classification?
Here is some discuss of coursera forum thread about confusion matrix and multi-class precision/recall measurement.
The basic idea is to compute all precision and recall of all the classes, then averag |
2,095 | How to compute precision/recall for multiclass-multilabel classification? | I don't know about the multi-label part but for the mutli-class classification
those links will help you
This link explains how to build the confusion matrix that you can use to calculate the precision and recall for each category
And this link explains how to calculate micro-f1 and macro-f1 measures to evaluate the c... | How to compute precision/recall for multiclass-multilabel classification? | I don't know about the multi-label part but for the mutli-class classification
those links will help you
This link explains how to build the confusion matrix that you can use to calculate the precisi | How to compute precision/recall for multiclass-multilabel classification?
I don't know about the multi-label part but for the mutli-class classification
those links will help you
This link explains how to build the confusion matrix that you can use to calculate the precision and recall for each category
And this link ... | How to compute precision/recall for multiclass-multilabel classification?
I don't know about the multi-label part but for the mutli-class classification
those links will help you
This link explains how to build the confusion matrix that you can use to calculate the precisi |
2,096 | How to compute precision/recall for multiclass-multilabel classification? | Exactly the same way you would do it general case, with sets:
http://en.wikipedia.org/wiki/F1_score
http://en.wikipedia.org/wiki/Precision_and_recall
Here are simple Python functions that do exactly that:
def precision(y_true, y_pred):
i = set(y_true).intersection(y_pred)
len1 = len(y_pred)
if len1 == 0:
... | How to compute precision/recall for multiclass-multilabel classification? | Exactly the same way you would do it general case, with sets:
http://en.wikipedia.org/wiki/F1_score
http://en.wikipedia.org/wiki/Precision_and_recall
Here are simple Python functions that do exactly t | How to compute precision/recall for multiclass-multilabel classification?
Exactly the same way you would do it general case, with sets:
http://en.wikipedia.org/wiki/F1_score
http://en.wikipedia.org/wiki/Precision_and_recall
Here are simple Python functions that do exactly that:
def precision(y_true, y_pred):
i = ... | How to compute precision/recall for multiclass-multilabel classification?
Exactly the same way you would do it general case, with sets:
http://en.wikipedia.org/wiki/F1_score
http://en.wikipedia.org/wiki/Precision_and_recall
Here are simple Python functions that do exactly t |
2,097 | How to compute precision/recall for multiclass-multilabel classification? | 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.
this link helped me.. https://www.youtube.com/watch?v=... | How to compute precision/recall for multiclass-multilabel classification? | 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.
| How to compute precision/recall for multiclass-multilabel classification?
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.
... | How to compute precision/recall for multiclass-multilabel classification?
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.
|
2,098 | How to compute precision/recall for multiclass-multilabel classification? | Check out these slides from cs205.org at Harvard. Once you get to the section on Error Measures, there is discussion of precision and recall in multi-class settings (e.g., one-vs-all or one-vs-one) and confusion matrices. Confusion matrices is what you really want here.
FYI, in the Python software package scikits.learn... | How to compute precision/recall for multiclass-multilabel classification? | Check out these slides from cs205.org at Harvard. Once you get to the section on Error Measures, there is discussion of precision and recall in multi-class settings (e.g., one-vs-all or one-vs-one) an | How to compute precision/recall for multiclass-multilabel classification?
Check out these slides from cs205.org at Harvard. Once you get to the section on Error Measures, there is discussion of precision and recall in multi-class settings (e.g., one-vs-all or one-vs-one) and confusion matrices. Confusion matrices is wh... | How to compute precision/recall for multiclass-multilabel classification?
Check out these slides from cs205.org at Harvard. Once you get to the section on Error Measures, there is discussion of precision and recall in multi-class settings (e.g., one-vs-all or one-vs-one) an |
2,099 | How to compute precision/recall for multiclass-multilabel classification? | From Ozgur et al (2005) it is possible to see that you should compute Precision and Recall following the normal expressions, but instead of averaging over total N instances in your dataset, you should use N=[instances with at least one label with the class in question assigned to].
here is the reference mentioned: http... | How to compute precision/recall for multiclass-multilabel classification? | From Ozgur et al (2005) it is possible to see that you should compute Precision and Recall following the normal expressions, but instead of averaging over total N instances in your dataset, you should | How to compute precision/recall for multiclass-multilabel classification?
From Ozgur et al (2005) it is possible to see that you should compute Precision and Recall following the normal expressions, but instead of averaging over total N instances in your dataset, you should use N=[instances with at least one label with... | How to compute precision/recall for multiclass-multilabel classification?
From Ozgur et al (2005) it is possible to see that you should compute Precision and Recall following the normal expressions, but instead of averaging over total N instances in your dataset, you should |
2,100 | How to compute precision/recall for multiclass-multilabel classification? | In case if you want to see the results directly:
from sklearn.metrics import classification_report, confusion_matrix
classification_report(y_test, y_pred)
This would work in case you want average precision, recall and f-1 score
from sklearn.metrics import precision_recall_fscore_support as score
precision,recall,fs... | How to compute precision/recall for multiclass-multilabel classification? | In case if you want to see the results directly:
from sklearn.metrics import classification_report, confusion_matrix
classification_report(y_test, y_pred)
This would work in case you want average pr | How to compute precision/recall for multiclass-multilabel classification?
In case if you want to see the results directly:
from sklearn.metrics import classification_report, confusion_matrix
classification_report(y_test, y_pred)
This would work in case you want average precision, recall and f-1 score
from sklearn.me... | How to compute precision/recall for multiclass-multilabel classification?
In case if you want to see the results directly:
from sklearn.metrics import classification_report, confusion_matrix
classification_report(y_test, y_pred)
This would work in case you want average pr |
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