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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7,501 | Should training samples randomly drawn for mini-batch training neural nets be drawn without replacement? | I loved your question because I found exactly the same issue. No one explicitly indicates if this is with or without replacement. Based on my own logic, it has to be with replacement since one epoch should contain all the training samples (by definition) and the following expression remains when training a NN (for exam... | Should training samples randomly drawn for mini-batch training neural nets be drawn without replacem | I loved your question because I found exactly the same issue. No one explicitly indicates if this is with or without replacement. Based on my own logic, it has to be with replacement since one epoch s | Should training samples randomly drawn for mini-batch training neural nets be drawn without replacement?
I loved your question because I found exactly the same issue. No one explicitly indicates if this is with or without replacement. Based on my own logic, it has to be with replacement since one epoch should contain a... | Should training samples randomly drawn for mini-batch training neural nets be drawn without replacem
I loved your question because I found exactly the same issue. No one explicitly indicates if this is with or without replacement. Based on my own logic, it has to be with replacement since one epoch s |
7,502 | Is there any difference between $r^2$ and $R^2$? | Notation on this matter seems to vary a little.
$R$ is used in the context of multiple correlation and is called the "multiple correlation coefficient". It is the correlation between the observed responses $Y$ and the $\hat Y$ fitted by the model. The $\hat Y$ is generally predicted from several predictor variables $X_... | Is there any difference between $r^2$ and $R^2$? | Notation on this matter seems to vary a little.
$R$ is used in the context of multiple correlation and is called the "multiple correlation coefficient". It is the correlation between the observed resp | Is there any difference between $r^2$ and $R^2$?
Notation on this matter seems to vary a little.
$R$ is used in the context of multiple correlation and is called the "multiple correlation coefficient". It is the correlation between the observed responses $Y$ and the $\hat Y$ fitted by the model. The $\hat Y$ is general... | Is there any difference between $r^2$ and $R^2$?
Notation on this matter seems to vary a little.
$R$ is used in the context of multiple correlation and is called the "multiple correlation coefficient". It is the correlation between the observed resp |
7,503 | Why are bias nodes used in neural networks? | The bias node in a neural network is a node that is always 'on'. That is, its value is set to $1$ without regard for the data in a given pattern. It is analogous to the intercept in a regression model, and serves the same function. If a neural network does not have a bias node in a given layer, it will not be able to... | Why are bias nodes used in neural networks? | The bias node in a neural network is a node that is always 'on'. That is, its value is set to $1$ without regard for the data in a given pattern. It is analogous to the intercept in a regression mod | Why are bias nodes used in neural networks?
The bias node in a neural network is a node that is always 'on'. That is, its value is set to $1$ without regard for the data in a given pattern. It is analogous to the intercept in a regression model, and serves the same function. If a neural network does not have a bias n... | Why are bias nodes used in neural networks?
The bias node in a neural network is a node that is always 'on'. That is, its value is set to $1$ without regard for the data in a given pattern. It is analogous to the intercept in a regression mod |
7,504 | Why are bias nodes used in neural networks? | In the context of neural networks, Batch Normalization is currently the gold-standard for making smart "bias nodes." Instead of clamping a neuron's bias value, you instead adjust for the covariance of the neuron's input. So in a CNN, you would apply a batch normalization just between the convolutional layer and the nex... | Why are bias nodes used in neural networks? | In the context of neural networks, Batch Normalization is currently the gold-standard for making smart "bias nodes." Instead of clamping a neuron's bias value, you instead adjust for the covariance of | Why are bias nodes used in neural networks?
In the context of neural networks, Batch Normalization is currently the gold-standard for making smart "bias nodes." Instead of clamping a neuron's bias value, you instead adjust for the covariance of the neuron's input. So in a CNN, you would apply a batch normalization just... | Why are bias nodes used in neural networks?
In the context of neural networks, Batch Normalization is currently the gold-standard for making smart "bias nodes." Instead of clamping a neuron's bias value, you instead adjust for the covariance of |
7,505 | Why are bias nodes used in neural networks? | Simple, short answers:
To shift the input function / be more flexible about the learned function.
A single bias node per layer.
Add them to all hidden layers and the input layer - with some footnotes
In a couple of experiments in my masters thesis (e.g. page 59), I found that the bias might be important for the first... | Why are bias nodes used in neural networks? | Simple, short answers:
To shift the input function / be more flexible about the learned function.
A single bias node per layer.
Add them to all hidden layers and the input layer - with some footnotes | Why are bias nodes used in neural networks?
Simple, short answers:
To shift the input function / be more flexible about the learned function.
A single bias node per layer.
Add them to all hidden layers and the input layer - with some footnotes
In a couple of experiments in my masters thesis (e.g. page 59), I found th... | Why are bias nodes used in neural networks?
Simple, short answers:
To shift the input function / be more flexible about the learned function.
A single bias node per layer.
Add them to all hidden layers and the input layer - with some footnotes |
7,506 | Gradient backpropagation through ResNet skip connections | Add sends the gradient back equally to both inputs. You can convince yourself of this by running the following in tensorflow:
import tensorflow as tf
graph = tf.Graph()
with graph.as_default():
x1_tf = tf.Variable(1.5, name='x1')
x2_tf = tf.Variable(3.5, name='x2')
out_tf = x1_tf + x2_tf
grads_tf = tf... | Gradient backpropagation through ResNet skip connections | Add sends the gradient back equally to both inputs. You can convince yourself of this by running the following in tensorflow:
import tensorflow as tf
graph = tf.Graph()
with graph.as_default():
x | Gradient backpropagation through ResNet skip connections
Add sends the gradient back equally to both inputs. You can convince yourself of this by running the following in tensorflow:
import tensorflow as tf
graph = tf.Graph()
with graph.as_default():
x1_tf = tf.Variable(1.5, name='x1')
x2_tf = tf.Variable(3.5,... | Gradient backpropagation through ResNet skip connections
Add sends the gradient back equally to both inputs. You can convince yourself of this by running the following in tensorflow:
import tensorflow as tf
graph = tf.Graph()
with graph.as_default():
x |
7,507 | Gradient backpropagation through ResNet skip connections | I'd like to recommend this limpid article: CS231n Convolutional Neural Networks for Visual Recognition, and let me compare the (simplified) vanilla network with the (simplified) residual network as follows.
Here is a diagram I borrowed from that page:
where the green numbers above the lines indicate the forward pass, ... | Gradient backpropagation through ResNet skip connections | I'd like to recommend this limpid article: CS231n Convolutional Neural Networks for Visual Recognition, and let me compare the (simplified) vanilla network with the (simplified) residual network as fo | Gradient backpropagation through ResNet skip connections
I'd like to recommend this limpid article: CS231n Convolutional Neural Networks for Visual Recognition, and let me compare the (simplified) vanilla network with the (simplified) residual network as follows.
Here is a diagram I borrowed from that page:
where the ... | Gradient backpropagation through ResNet skip connections
I'd like to recommend this limpid article: CS231n Convolutional Neural Networks for Visual Recognition, and let me compare the (simplified) vanilla network with the (simplified) residual network as fo |
7,508 | Cross-validation misuse (reporting performance for the best hyperparameter value) | Yes, there are issues with reporting only k-fold CV results. You could use e.g. the following three publications for your purpose (though there are more out there, of course) to point people towards the right direction:
Varma & Simon (2006). "Bias in error estimation when using cross-validation for model selection." B... | Cross-validation misuse (reporting performance for the best hyperparameter value) | Yes, there are issues with reporting only k-fold CV results. You could use e.g. the following three publications for your purpose (though there are more out there, of course) to point people towards t | Cross-validation misuse (reporting performance for the best hyperparameter value)
Yes, there are issues with reporting only k-fold CV results. You could use e.g. the following three publications for your purpose (though there are more out there, of course) to point people towards the right direction:
Varma & Simon (20... | Cross-validation misuse (reporting performance for the best hyperparameter value)
Yes, there are issues with reporting only k-fold CV results. You could use e.g. the following three publications for your purpose (though there are more out there, of course) to point people towards t |
7,509 | predict() Function for lmer Mixed Effects Models | It's easy to get confused by the presentation of coefficients when you call coef(fit2). Look at the summary of fit2:
> summary(fit2)
Linear mixed model fit by REML ['lmerMod']
Formula: Recall ~ (1 | Subject/Time) + Caffeine
Data: data
REML criterion at convergence: 444.5
Scaled residuals:
Min 1Q Median... | predict() Function for lmer Mixed Effects Models | It's easy to get confused by the presentation of coefficients when you call coef(fit2). Look at the summary of fit2:
> summary(fit2)
Linear mixed model fit by REML ['lmerMod']
Formula: Recall ~ (1 | | predict() Function for lmer Mixed Effects Models
It's easy to get confused by the presentation of coefficients when you call coef(fit2). Look at the summary of fit2:
> summary(fit2)
Linear mixed model fit by REML ['lmerMod']
Formula: Recall ~ (1 | Subject/Time) + Caffeine
Data: data
REML criterion at convergence:... | predict() Function for lmer Mixed Effects Models
It's easy to get confused by the presentation of coefficients when you call coef(fit2). Look at the summary of fit2:
> summary(fit2)
Linear mixed model fit by REML ['lmerMod']
Formula: Recall ~ (1 | |
7,510 | Multiple comparisons on a mixed effects model | If time and Genotype are both categorical predictors as they appear to be, and you are interested in comparing all time/Genotype pairs to each other, then you can just create one interaction variable, and use Tukey contrasts on it:
weights$TimeGeno <- interaction(weigths$Time, weights$Geno)
model <- lme(weight ~ TimeGe... | Multiple comparisons on a mixed effects model | If time and Genotype are both categorical predictors as they appear to be, and you are interested in comparing all time/Genotype pairs to each other, then you can just create one interaction variable, | Multiple comparisons on a mixed effects model
If time and Genotype are both categorical predictors as they appear to be, and you are interested in comparing all time/Genotype pairs to each other, then you can just create one interaction variable, and use Tukey contrasts on it:
weights$TimeGeno <- interaction(weigths$Ti... | Multiple comparisons on a mixed effects model
If time and Genotype are both categorical predictors as they appear to be, and you are interested in comparing all time/Genotype pairs to each other, then you can just create one interaction variable, |
7,511 | Mathematical differences between GBM, XGBoost, LightGBM, CatBoost? | My guess is that catboost doesn't use the dummified variables, so the weight given to each (categorical) variable is more balanced compared to the other implementations, so the high-cardinality variables don't have more weight than the others.
https://arxiv.org/abs/1706.09516
You want to look at this English language ... | Mathematical differences between GBM, XGBoost, LightGBM, CatBoost? | My guess is that catboost doesn't use the dummified variables, so the weight given to each (categorical) variable is more balanced compared to the other implementations, so the high-cardinality variab | Mathematical differences between GBM, XGBoost, LightGBM, CatBoost?
My guess is that catboost doesn't use the dummified variables, so the weight given to each (categorical) variable is more balanced compared to the other implementations, so the high-cardinality variables don't have more weight than the others.
https://... | Mathematical differences between GBM, XGBoost, LightGBM, CatBoost?
My guess is that catboost doesn't use the dummified variables, so the weight given to each (categorical) variable is more balanced compared to the other implementations, so the high-cardinality variab |
7,512 | Intuition behind standard deviation | My intuition is that the standard deviation is: a measure of spread of the data.
You have a good point that whether it is wide, or tight depends on what our underlying assumption is for the distribution of the data.
Caveat: A measure of spread is most helpful when the distribution of your data is symmetric around the m... | Intuition behind standard deviation | My intuition is that the standard deviation is: a measure of spread of the data.
You have a good point that whether it is wide, or tight depends on what our underlying assumption is for the distributi | Intuition behind standard deviation
My intuition is that the standard deviation is: a measure of spread of the data.
You have a good point that whether it is wide, or tight depends on what our underlying assumption is for the distribution of the data.
Caveat: A measure of spread is most helpful when the distribution of... | Intuition behind standard deviation
My intuition is that the standard deviation is: a measure of spread of the data.
You have a good point that whether it is wide, or tight depends on what our underlying assumption is for the distributi |
7,513 | Intuition behind standard deviation | It may help to realize that the mean is analogous to the center of mass. The variance is the moment of inertia. The standard deviation is the radius of gyration.
For a historical perspective, take a look at:
George Airy (1875) On the algebraical and numerical theory of errors of observations and the combination of obse... | Intuition behind standard deviation | It may help to realize that the mean is analogous to the center of mass. The variance is the moment of inertia. The standard deviation is the radius of gyration.
For a historical perspective, take a l | Intuition behind standard deviation
It may help to realize that the mean is analogous to the center of mass. The variance is the moment of inertia. The standard deviation is the radius of gyration.
For a historical perspective, take a look at:
George Airy (1875) On the algebraical and numerical theory of errors of obse... | Intuition behind standard deviation
It may help to realize that the mean is analogous to the center of mass. The variance is the moment of inertia. The standard deviation is the radius of gyration.
For a historical perspective, take a l |
7,514 | Intuition behind standard deviation | The standard deviation does, indeed, give more weight to those farther from the mean, because it is the square root of the average of the squared distances. The reasons for using this (rather than the mean absolute deviation that you propose, or the median absolute deviation, which is used in robust statistics) are par... | Intuition behind standard deviation | The standard deviation does, indeed, give more weight to those farther from the mean, because it is the square root of the average of the squared distances. The reasons for using this (rather than the | Intuition behind standard deviation
The standard deviation does, indeed, give more weight to those farther from the mean, because it is the square root of the average of the squared distances. The reasons for using this (rather than the mean absolute deviation that you propose, or the median absolute deviation, which i... | Intuition behind standard deviation
The standard deviation does, indeed, give more weight to those farther from the mean, because it is the square root of the average of the squared distances. The reasons for using this (rather than the |
7,515 | Intuition behind standard deviation | Standard deviation measures the distance of your population from the mean as random variables.
Let us suppose that your 5 numbers are equally likely to have occurred, so that each has probability .20. This is represented by the random variable $X: [0,1] \rightarrow \mathbb{R}$ given by
$$X(t) = \begin{cases} 1 & 0 \l... | Intuition behind standard deviation | Standard deviation measures the distance of your population from the mean as random variables.
Let us suppose that your 5 numbers are equally likely to have occurred, so that each has probability .20. | Intuition behind standard deviation
Standard deviation measures the distance of your population from the mean as random variables.
Let us suppose that your 5 numbers are equally likely to have occurred, so that each has probability .20. This is represented by the random variable $X: [0,1] \rightarrow \mathbb{R}$ give... | Intuition behind standard deviation
Standard deviation measures the distance of your population from the mean as random variables.
Let us suppose that your 5 numbers are equally likely to have occurred, so that each has probability .20. |
7,516 | What concepts/objects are "wrongly" formed in probability and statistics? | The chi-squared distribution is wrong!
The two main statistical uses of the chi-square distribution are to give the asymptotic (and in some cases exact) distribution of variance estimators, and to give the asymptotic distribution of test statistics relating to squared deviations. In both cases the chi-squared distrib... | What concepts/objects are "wrongly" formed in probability and statistics? | The chi-squared distribution is wrong!
The two main statistical uses of the chi-square distribution are to give the asymptotic (and in some cases exact) distribution of variance estimators, and to gi | What concepts/objects are "wrongly" formed in probability and statistics?
The chi-squared distribution is wrong!
The two main statistical uses of the chi-square distribution are to give the asymptotic (and in some cases exact) distribution of variance estimators, and to give the asymptotic distribution of test statist... | What concepts/objects are "wrongly" formed in probability and statistics?
The chi-squared distribution is wrong!
The two main statistical uses of the chi-square distribution are to give the asymptotic (and in some cases exact) distribution of variance estimators, and to gi |
7,517 | How to statistically compare the performance of machine learning classifiers? | A review and critique of some t-test approaches is given in Choosing between two learning algorithms based on calibrated tests, Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms, and On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach | How to statistically compare the performance of machine learning classifiers? | A review and critique of some t-test approaches is given in Choosing between two learning algorithms based on calibrated tests, Approximate Statistical Tests for Comparing Supervised Classification Le | How to statistically compare the performance of machine learning classifiers?
A review and critique of some t-test approaches is given in Choosing between two learning algorithms based on calibrated tests, Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms, and On Comparing Classi... | How to statistically compare the performance of machine learning classifiers?
A review and critique of some t-test approaches is given in Choosing between two learning algorithms based on calibrated tests, Approximate Statistical Tests for Comparing Supervised Classification Le |
7,518 | How to statistically compare the performance of machine learning classifiers? | I don't have the Fleiss book at hand, so all this is IIRC.
Answering @JohnMoeller's question in the comments for the moment: the original question is IMHO unanswerable as it is.
So suppose that I have 30 samples, and I test c1 and c2 on each sample, and record the accuracy for each on each sample.
doing this, you e... | How to statistically compare the performance of machine learning classifiers? | I don't have the Fleiss book at hand, so all this is IIRC.
Answering @JohnMoeller's question in the comments for the moment: the original question is IMHO unanswerable as it is.
So suppose that I hav | How to statistically compare the performance of machine learning classifiers?
I don't have the Fleiss book at hand, so all this is IIRC.
Answering @JohnMoeller's question in the comments for the moment: the original question is IMHO unanswerable as it is.
So suppose that I have 30 samples, and I test c1 and c2 on each... | How to statistically compare the performance of machine learning classifiers?
I don't have the Fleiss book at hand, so all this is IIRC.
Answering @JohnMoeller's question in the comments for the moment: the original question is IMHO unanswerable as it is.
So suppose that I hav |
7,519 | How does batch size affect convergence of SGD and why? | Sure one update with a big minibatch is "better" (in terms of accuracy) than one update with a small minibatch. This can be seen in the table you copied in your question (call $N$ the sample size):
batch size 1: number of updates $27N$
batch size 20,000: number of updates $8343\times\frac{N}{20000}\approx 0.47N$
You... | How does batch size affect convergence of SGD and why? | Sure one update with a big minibatch is "better" (in terms of accuracy) than one update with a small minibatch. This can be seen in the table you copied in your question (call $N$ the sample size):
| How does batch size affect convergence of SGD and why?
Sure one update with a big minibatch is "better" (in terms of accuracy) than one update with a small minibatch. This can be seen in the table you copied in your question (call $N$ the sample size):
batch size 1: number of updates $27N$
batch size 20,000: number o... | How does batch size affect convergence of SGD and why?
Sure one update with a big minibatch is "better" (in terms of accuracy) than one update with a small minibatch. This can be seen in the table you copied in your question (call $N$ the sample size):
|
7,520 | How does batch size affect convergence of SGD and why? | To add to Curtis White's answer (and adding some more references):
Yes SGD works as a type of regularization. This is important because otherwise, it's hard to explain why DNNs do not always overfit, because they can.
The reason, as I understand, is that SGD causes 'hopping around' in parameter space, so during train... | How does batch size affect convergence of SGD and why? | To add to Curtis White's answer (and adding some more references):
Yes SGD works as a type of regularization. This is important because otherwise, it's hard to explain why DNNs do not always overfit, | How does batch size affect convergence of SGD and why?
To add to Curtis White's answer (and adding some more references):
Yes SGD works as a type of regularization. This is important because otherwise, it's hard to explain why DNNs do not always overfit, because they can.
The reason, as I understand, is that SGD caus... | How does batch size affect convergence of SGD and why?
To add to Curtis White's answer (and adding some more references):
Yes SGD works as a type of regularization. This is important because otherwise, it's hard to explain why DNNs do not always overfit, |
7,521 | How does batch size affect convergence of SGD and why? | A too large batch size can prevent convergence at least when using SGD and training MLP using Keras. As for why, I am not 100% sure whether it has to do with averaging of the gradients or that smaller updates provides greater probability of escaping the local minima.
See here. | How does batch size affect convergence of SGD and why? | A too large batch size can prevent convergence at least when using SGD and training MLP using Keras. As for why, I am not 100% sure whether it has to do with averaging of the gradients or that smaller | How does batch size affect convergence of SGD and why?
A too large batch size can prevent convergence at least when using SGD and training MLP using Keras. As for why, I am not 100% sure whether it has to do with averaging of the gradients or that smaller updates provides greater probability of escaping the local minim... | How does batch size affect convergence of SGD and why?
A too large batch size can prevent convergence at least when using SGD and training MLP using Keras. As for why, I am not 100% sure whether it has to do with averaging of the gradients or that smaller |
7,522 | How does batch size affect convergence of SGD and why? | From my experience with tensorflow and keras (which is confirmed by this article):
Higher batchsize results lower accuracy
Accuracy can be kept up if learning rate is increased when batchsize is high | How does batch size affect convergence of SGD and why? | From my experience with tensorflow and keras (which is confirmed by this article):
Higher batchsize results lower accuracy
Accuracy can be kept up if learning rate is increased when batchsize is high | How does batch size affect convergence of SGD and why?
From my experience with tensorflow and keras (which is confirmed by this article):
Higher batchsize results lower accuracy
Accuracy can be kept up if learning rate is increased when batchsize is high | How does batch size affect convergence of SGD and why?
From my experience with tensorflow and keras (which is confirmed by this article):
Higher batchsize results lower accuracy
Accuracy can be kept up if learning rate is increased when batchsize is high |
7,523 | Comparison between SHAP (Shapley Additive Explanation) and LIME (Local Interpretable Model-Agnostic Explanations) | LIME creates a surrogate model locally around the unit whose prediction you wish to understand. Thus it is inherently local.
Shapley values 'decompose' the final prediction into the contribution of each attribute - this is what some mean by 'consistent' (the values add up to the actual prediction of the true model, an... | Comparison between SHAP (Shapley Additive Explanation) and LIME (Local Interpretable Model-Agnostic | LIME creates a surrogate model locally around the unit whose prediction you wish to understand. Thus it is inherently local.
Shapley values 'decompose' the final prediction into the contribution of e | Comparison between SHAP (Shapley Additive Explanation) and LIME (Local Interpretable Model-Agnostic Explanations)
LIME creates a surrogate model locally around the unit whose prediction you wish to understand. Thus it is inherently local.
Shapley values 'decompose' the final prediction into the contribution of each at... | Comparison between SHAP (Shapley Additive Explanation) and LIME (Local Interpretable Model-Agnostic
LIME creates a surrogate model locally around the unit whose prediction you wish to understand. Thus it is inherently local.
Shapley values 'decompose' the final prediction into the contribution of e |
7,524 | What are the relative merits of Winsorizing vs. Trimming data? | In a different, but related question on trimming that I just stumbled across, one answer had the following helpful insight into why one might use either winsorizing or trimming:
If you take the trimmed distribution, you explicitly state: I am not
interested in outliers/ the tails of the distribution. If you believe
... | What are the relative merits of Winsorizing vs. Trimming data? | In a different, but related question on trimming that I just stumbled across, one answer had the following helpful insight into why one might use either winsorizing or trimming:
If you take the trimm | What are the relative merits of Winsorizing vs. Trimming data?
In a different, but related question on trimming that I just stumbled across, one answer had the following helpful insight into why one might use either winsorizing or trimming:
If you take the trimmed distribution, you explicitly state: I am not
interes... | What are the relative merits of Winsorizing vs. Trimming data?
In a different, but related question on trimming that I just stumbled across, one answer had the following helpful insight into why one might use either winsorizing or trimming:
If you take the trimm |
7,525 | What are the relative merits of Winsorizing vs. Trimming data? | Clearly, the respective merits depend on the data under analysis, and although they depend in non-trivial ways on what actually causes data to be distributed as it is, we can at least consider two extreme cases.
Data is virtually error-free, it just has legitimate outliers, but you don't want your results to be severe... | What are the relative merits of Winsorizing vs. Trimming data? | Clearly, the respective merits depend on the data under analysis, and although they depend in non-trivial ways on what actually causes data to be distributed as it is, we can at least consider two ext | What are the relative merits of Winsorizing vs. Trimming data?
Clearly, the respective merits depend on the data under analysis, and although they depend in non-trivial ways on what actually causes data to be distributed as it is, we can at least consider two extreme cases.
Data is virtually error-free, it just has le... | What are the relative merits of Winsorizing vs. Trimming data?
Clearly, the respective merits depend on the data under analysis, and although they depend in non-trivial ways on what actually causes data to be distributed as it is, we can at least consider two ext |
7,526 | What are the relative merits of Winsorizing vs. Trimming data? | A good question that is faced very often in all fields!
In either case you are technically removing them from the data set.
I know it is common practice when trying to find a trend graphically to use a form of truncation: use the whole data set for plotting purposes, but then exclude the extreme values for the interpre... | What are the relative merits of Winsorizing vs. Trimming data? | A good question that is faced very often in all fields!
In either case you are technically removing them from the data set.
I know it is common practice when trying to find a trend graphically to use | What are the relative merits of Winsorizing vs. Trimming data?
A good question that is faced very often in all fields!
In either case you are technically removing them from the data set.
I know it is common practice when trying to find a trend graphically to use a form of truncation: use the whole data set for plotting... | What are the relative merits of Winsorizing vs. Trimming data?
A good question that is faced very often in all fields!
In either case you are technically removing them from the data set.
I know it is common practice when trying to find a trend graphically to use |
7,527 | What are the relative merits of Winsorizing vs. Trimming data? | This is a good question, and one I have been faced with. In cases where you have a large dataset or a more accurately a largely varying dataset, where the minority of data values vary across a wide scale (but nevertheless are required to be shown), and the majority of the dataset is within a narrow band, such that if t... | What are the relative merits of Winsorizing vs. Trimming data? | This is a good question, and one I have been faced with. In cases where you have a large dataset or a more accurately a largely varying dataset, where the minority of data values vary across a wide sc | What are the relative merits of Winsorizing vs. Trimming data?
This is a good question, and one I have been faced with. In cases where you have a large dataset or a more accurately a largely varying dataset, where the minority of data values vary across a wide scale (but nevertheless are required to be shown), and the ... | What are the relative merits of Winsorizing vs. Trimming data?
This is a good question, and one I have been faced with. In cases where you have a large dataset or a more accurately a largely varying dataset, where the minority of data values vary across a wide sc |
7,528 | What are the relative merits of Winsorizing vs. Trimming data? | One advantage of Winsorizing is that the calculation may be more efficient. In order to calculate a true truncated mean, you need to sort all of the data elements, and that is typically $O(n \log n)$. However there are efficient ways of figuring out just the 25% and 75% percentiles using a the quick select algorithm, ... | What are the relative merits of Winsorizing vs. Trimming data? | One advantage of Winsorizing is that the calculation may be more efficient. In order to calculate a true truncated mean, you need to sort all of the data elements, and that is typically $O(n \log n)$ | What are the relative merits of Winsorizing vs. Trimming data?
One advantage of Winsorizing is that the calculation may be more efficient. In order to calculate a true truncated mean, you need to sort all of the data elements, and that is typically $O(n \log n)$. However there are efficient ways of figuring out just t... | What are the relative merits of Winsorizing vs. Trimming data?
One advantage of Winsorizing is that the calculation may be more efficient. In order to calculate a true truncated mean, you need to sort all of the data elements, and that is typically $O(n \log n)$ |
7,529 | Training a neural network for regression always predicts the mean | I am going to contradict @Pieter's answer and say that your problem is that you have too much bias and too little variance. In other words, your network is not complex enough for this task.
To see this, let $Y$ be the true and correct output that your network should return (the target), and let $\hat{Y}$ be the output ... | Training a neural network for regression always predicts the mean | I am going to contradict @Pieter's answer and say that your problem is that you have too much bias and too little variance. In other words, your network is not complex enough for this task.
To see thi | Training a neural network for regression always predicts the mean
I am going to contradict @Pieter's answer and say that your problem is that you have too much bias and too little variance. In other words, your network is not complex enough for this task.
To see this, let $Y$ be the true and correct output that your ne... | Training a neural network for regression always predicts the mean
I am going to contradict @Pieter's answer and say that your problem is that you have too much bias and too little variance. In other words, your network is not complex enough for this task.
To see thi |
7,530 | Training a neural network for regression always predicts the mean | It means that your model decides to "do nothing" rather than prediction. And you should know that "doing nothing" will get a lower error than a random guess for the regression.
For example, if the true values are in the range [-10 10], then keep predicting 0 will return you about 2.5 mean absolute error; In contrast, i... | Training a neural network for regression always predicts the mean | It means that your model decides to "do nothing" rather than prediction. And you should know that "doing nothing" will get a lower error than a random guess for the regression.
For example, if the tru | Training a neural network for regression always predicts the mean
It means that your model decides to "do nothing" rather than prediction. And you should know that "doing nothing" will get a lower error than a random guess for the regression.
For example, if the true values are in the range [-10 10], then keep predicti... | Training a neural network for regression always predicts the mean
It means that your model decides to "do nothing" rather than prediction. And you should know that "doing nothing" will get a lower error than a random guess for the regression.
For example, if the tru |
7,531 | Training a neural network for regression always predicts the mean | I was actually working on a very similar problem. Basically, I had a bunch of dots on a white background and I was training a NN to recognize the dot that was placed on the background first. The way I found to work was to just use one fully-connected layer of neurons (so a 1-layer NN). For example, for a 100x100 image,... | Training a neural network for regression always predicts the mean | I was actually working on a very similar problem. Basically, I had a bunch of dots on a white background and I was training a NN to recognize the dot that was placed on the background first. The way I | Training a neural network for regression always predicts the mean
I was actually working on a very similar problem. Basically, I had a bunch of dots on a white background and I was training a NN to recognize the dot that was placed on the background first. The way I found to work was to just use one fully-connected lay... | Training a neural network for regression always predicts the mean
I was actually working on a very similar problem. Basically, I had a bunch of dots on a white background and I was training a NN to recognize the dot that was placed on the background first. The way I |
7,532 | Training a neural network for regression always predicts the mean | I am facing the same problem with my data set. It turns out that in my case the predictors are highly concentrated with a very small variance. You should check out the variance of your prediction variables and see how it is distributed.
However, some transformations on the output variable can be performed to modify or... | Training a neural network for regression always predicts the mean | I am facing the same problem with my data set. It turns out that in my case the predictors are highly concentrated with a very small variance. You should check out the variance of your prediction vari | Training a neural network for regression always predicts the mean
I am facing the same problem with my data set. It turns out that in my case the predictors are highly concentrated with a very small variance. You should check out the variance of your prediction variables and see how it is distributed.
However, some tr... | Training a neural network for regression always predicts the mean
I am facing the same problem with my data set. It turns out that in my case the predictors are highly concentrated with a very small variance. You should check out the variance of your prediction vari |
7,533 | Training a neural network for regression always predicts the mean | Here is a more information-theoretic reason for why this is happening. Let $Y$ be the true and correct output that your network should return (the target), and let $\hat{Y}$ be the output that your network actually returns. The problem that you observe is that $\hat{Y}=K$, where $K$ is some constant, which means that $... | Training a neural network for regression always predicts the mean | Here is a more information-theoretic reason for why this is happening. Let $Y$ be the true and correct output that your network should return (the target), and let $\hat{Y}$ be the output that your ne | Training a neural network for regression always predicts the mean
Here is a more information-theoretic reason for why this is happening. Let $Y$ be the true and correct output that your network should return (the target), and let $\hat{Y}$ be the output that your network actually returns. The problem that you observe i... | Training a neural network for regression always predicts the mean
Here is a more information-theoretic reason for why this is happening. Let $Y$ be the true and correct output that your network should return (the target), and let $\hat{Y}$ be the output that your ne |
7,534 | Training a neural network for regression always predicts the mean | It looks like a typical overfitting problem. Your data does not provide enough information to get the better result. You choose the complex NN with you train to remember all nuances of the train data. Loss can never be a zero, as it is on your graph. BTW It seems your validation has a bug or validation set is not a goo... | Training a neural network for regression always predicts the mean | It looks like a typical overfitting problem. Your data does not provide enough information to get the better result. You choose the complex NN with you train to remember all nuances of the train data. | Training a neural network for regression always predicts the mean
It looks like a typical overfitting problem. Your data does not provide enough information to get the better result. You choose the complex NN with you train to remember all nuances of the train data. Loss can never be a zero, as it is on your graph. BTW... | Training a neural network for regression always predicts the mean
It looks like a typical overfitting problem. Your data does not provide enough information to get the better result. You choose the complex NN with you train to remember all nuances of the train data. |
7,535 | What statistical blogs would you recommend? | http://www.r-bloggers.com/ is an aggregated blog from lots of blogs that talk about statistics using R, and the #rstats hashtag on twitter is also helpful. I write quite a bit about statistics and R in genetics research. | What statistical blogs would you recommend? | http://www.r-bloggers.com/ is an aggregated blog from lots of blogs that talk about statistics using R, and the #rstats hashtag on twitter is also helpful. I write quite a bit about statistics and R i | What statistical blogs would you recommend?
http://www.r-bloggers.com/ is an aggregated blog from lots of blogs that talk about statistics using R, and the #rstats hashtag on twitter is also helpful. I write quite a bit about statistics and R in genetics research. | What statistical blogs would you recommend?
http://www.r-bloggers.com/ is an aggregated blog from lots of blogs that talk about statistics using R, and the #rstats hashtag on twitter is also helpful. I write quite a bit about statistics and R i |
7,536 | What statistical blogs would you recommend? | Statistical Modeling, Causal Inference, and Social Science from Andrew Gelman is a good blog. | What statistical blogs would you recommend? | Statistical Modeling, Causal Inference, and Social Science from Andrew Gelman is a good blog. | What statistical blogs would you recommend?
Statistical Modeling, Causal Inference, and Social Science from Andrew Gelman is a good blog. | What statistical blogs would you recommend?
Statistical Modeling, Causal Inference, and Social Science from Andrew Gelman is a good blog. |
7,537 | What statistical blogs would you recommend? | The Endeavour sometimes features statistics posts. Otherwise it is mostly around the interplay of computer science and math. | What statistical blogs would you recommend? | The Endeavour sometimes features statistics posts. Otherwise it is mostly around the interplay of computer science and math. | What statistical blogs would you recommend?
The Endeavour sometimes features statistics posts. Otherwise it is mostly around the interplay of computer science and math. | What statistical blogs would you recommend?
The Endeavour sometimes features statistics posts. Otherwise it is mostly around the interplay of computer science and math. |
7,538 | What statistical blogs would you recommend? | In addition to those already mentioned, I like Rob Hyndman's blog:
http://robjhyndman.com/researchtips/
I guess he's too modest to mention it himself! ;-) | What statistical blogs would you recommend? | In addition to those already mentioned, I like Rob Hyndman's blog:
http://robjhyndman.com/researchtips/
I guess he's too modest to mention it himself! ;-) | What statistical blogs would you recommend?
In addition to those already mentioned, I like Rob Hyndman's blog:
http://robjhyndman.com/researchtips/
I guess he's too modest to mention it himself! ;-) | What statistical blogs would you recommend?
In addition to those already mentioned, I like Rob Hyndman's blog:
http://robjhyndman.com/researchtips/
I guess he's too modest to mention it himself! ;-) |
7,539 | What statistical blogs would you recommend? | Cosma Shalizi's blog, often talks about statistics, and is always interesting. | What statistical blogs would you recommend? | Cosma Shalizi's blog, often talks about statistics, and is always interesting. | What statistical blogs would you recommend?
Cosma Shalizi's blog, often talks about statistics, and is always interesting. | What statistical blogs would you recommend?
Cosma Shalizi's blog, often talks about statistics, and is always interesting. |
7,540 | What statistical blogs would you recommend? | 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.
XI'AN'S OG | What statistical blogs would you recommend? | 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 statistical blogs would you recommend?
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.
XI'AN'S OG | What statistical blogs would you recommend?
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.
|
7,541 | What statistical blogs would you recommend? | Darren Wilkinson's research blog | What statistical blogs would you recommend? | Darren Wilkinson's research blog | What statistical blogs would you recommend?
Darren Wilkinson's research blog | What statistical blogs would you recommend?
Darren Wilkinson's research blog |
7,542 | What statistical blogs would you recommend? | I like Dave Giles' blog Econometrics Beat It has a time series focus, but lots of other interesting things as well. Here's a nice post on the Pythagorean means. | What statistical blogs would you recommend? | I like Dave Giles' blog Econometrics Beat It has a time series focus, but lots of other interesting things as well. Here's a nice post on the Pythagorean means. | What statistical blogs would you recommend?
I like Dave Giles' blog Econometrics Beat It has a time series focus, but lots of other interesting things as well. Here's a nice post on the Pythagorean means. | What statistical blogs would you recommend?
I like Dave Giles' blog Econometrics Beat It has a time series focus, but lots of other interesting things as well. Here's a nice post on the Pythagorean means. |
7,543 | What statistical blogs would you recommend? | simplystatistics.org is rather good. It is run by three biostatistics professors (Jeff Leek, Roger Peng, and Rafa Irizarry).
http://simplystatistics.org/ | What statistical blogs would you recommend? | simplystatistics.org is rather good. It is run by three biostatistics professors (Jeff Leek, Roger Peng, and Rafa Irizarry).
http://simplystatistics.org/ | What statistical blogs would you recommend?
simplystatistics.org is rather good. It is run by three biostatistics professors (Jeff Leek, Roger Peng, and Rafa Irizarry).
http://simplystatistics.org/ | What statistical blogs would you recommend?
simplystatistics.org is rather good. It is run by three biostatistics professors (Jeff Leek, Roger Peng, and Rafa Irizarry).
http://simplystatistics.org/ |
7,544 | What statistical blogs would you recommend? | Luckily now you don't have to spend too much effort finding good blogs. Stats Blogs is a relatively new aggregator which compiles a collection of blogs focused on statistics. A lot of the blogs mentioned are aggregated there. | What statistical blogs would you recommend? | Luckily now you don't have to spend too much effort finding good blogs. Stats Blogs is a relatively new aggregator which compiles a collection of blogs focused on statistics. A lot of the blogs ment | What statistical blogs would you recommend?
Luckily now you don't have to spend too much effort finding good blogs. Stats Blogs is a relatively new aggregator which compiles a collection of blogs focused on statistics. A lot of the blogs mentioned are aggregated there. | What statistical blogs would you recommend?
Luckily now you don't have to spend too much effort finding good blogs. Stats Blogs is a relatively new aggregator which compiles a collection of blogs focused on statistics. A lot of the blogs ment |
7,545 | What statistical blogs would you recommend? | http://blog.thegrandlocus.com/
This blog mixes basic concepts of statistics, everyday life and research. A must-read. | What statistical blogs would you recommend? | http://blog.thegrandlocus.com/
This blog mixes basic concepts of statistics, everyday life and research. A must-read. | What statistical blogs would you recommend?
http://blog.thegrandlocus.com/
This blog mixes basic concepts of statistics, everyday life and research. A must-read. | What statistical blogs would you recommend?
http://blog.thegrandlocus.com/
This blog mixes basic concepts of statistics, everyday life and research. A must-read. |
7,546 | What statistical blogs would you recommend? | FlowingData is a good read, with an emphasis on data visualization | What statistical blogs would you recommend? | FlowingData is a good read, with an emphasis on data visualization | What statistical blogs would you recommend?
FlowingData is a good read, with an emphasis on data visualization | What statistical blogs would you recommend?
FlowingData is a good read, with an emphasis on data visualization |
7,547 | What statistical blogs would you recommend? | Statistical Ideas at statisticalideas@blogspot.com for a mix of applied finance and theory. | What statistical blogs would you recommend? | Statistical Ideas at statisticalideas@blogspot.com for a mix of applied finance and theory. | What statistical blogs would you recommend?
Statistical Ideas at statisticalideas@blogspot.com for a mix of applied finance and theory. | What statistical blogs would you recommend?
Statistical Ideas at statisticalideas@blogspot.com for a mix of applied finance and theory. |
7,548 | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner? | For any one relationship, the odds of sharing the same month and day are approximately 1 in 365 (not exactly because of leap year and because births are not exactly evenly spaced within a year. If you add in year, it's probably something like 1 in 3000 or 4000 (most people have relationships with people relatively clos... | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner? | For any one relationship, the odds of sharing the same month and day are approximately 1 in 365 (not exactly because of leap year and because births are not exactly evenly spaced within a year. If you | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner?
For any one relationship, the odds of sharing the same month and day are approximately 1 in 365 (not exactly because of leap year and because births are not exactly evenly spaced within a year. If you add in year, it's p... | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner?
For any one relationship, the odds of sharing the same month and day are approximately 1 in 365 (not exactly because of leap year and because births are not exactly evenly spaced within a year. If you |
7,549 | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner? | As Peter pointed out, it is impossible to calculate coincidences after the fact.
Your question got me thinking, and I realized my girlfriend and I also have a strange birthday coincidence. She was born exactly 432 days before me! And we are also in a successful relationship!
I don't know what this probability is, but... | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner? | As Peter pointed out, it is impossible to calculate coincidences after the fact.
Your question got me thinking, and I realized my girlfriend and I also have a strange birthday coincidence. She was bo | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner?
As Peter pointed out, it is impossible to calculate coincidences after the fact.
Your question got me thinking, and I realized my girlfriend and I also have a strange birthday coincidence. She was born exactly 432 days ... | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner?
As Peter pointed out, it is impossible to calculate coincidences after the fact.
Your question got me thinking, and I realized my girlfriend and I also have a strange birthday coincidence. She was bo |
7,550 | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner? | So, first of all, the odds of sharing some weird connection with any random person are probably quite high. From experience I'd guess around 20% or so, no way to seriously calculate that, but no matter what it exactly is, just want to be clear having a special weird connection means nothing (though it is fun).
Then, so... | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner? | So, first of all, the odds of sharing some weird connection with any random person are probably quite high. From experience I'd guess around 20% or so, no way to seriously calculate that, but no matte | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner?
So, first of all, the odds of sharing some weird connection with any random person are probably quite high. From experience I'd guess around 20% or so, no way to seriously calculate that, but no matter what it exactly is... | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner?
So, first of all, the odds of sharing some weird connection with any random person are probably quite high. From experience I'd guess around 20% or so, no way to seriously calculate that, but no matte |
7,551 | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner? | If it's an event specified before the fact, you can simply break it down:
The chance that your boyfriend was born the same year as you is actually very high (especially given many situations tend to bring people of very similar age together); it's a very difficult probability to calculate, though, without data.
If you... | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner? | If it's an event specified before the fact, you can simply break it down:
The chance that your boyfriend was born the same year as you is actually very high (especially given many situations tend to b | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner?
If it's an event specified before the fact, you can simply break it down:
The chance that your boyfriend was born the same year as you is actually very high (especially given many situations tend to bring people of very ... | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner?
If it's an event specified before the fact, you can simply break it down:
The chance that your boyfriend was born the same year as you is actually very high (especially given many situations tend to b |
7,552 | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner? | Taking the question literally
According to wikipedia, 33.2% of married couples in the United States differ in age by less than one year.
Thus, a baseline estimate for sharing the same date of birth would be the above statistic divided by two (because it captures 2 years) for sharing the same year multiplied by the prob... | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner? | Taking the question literally
According to wikipedia, 33.2% of married couples in the United States differ in age by less than one year.
Thus, a baseline estimate for sharing the same date of birth wo | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner?
Taking the question literally
According to wikipedia, 33.2% of married couples in the United States differ in age by less than one year.
Thus, a baseline estimate for sharing the same date of birth would be the above sta... | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner?
Taking the question literally
According to wikipedia, 33.2% of married couples in the United States differ in age by less than one year.
Thus, a baseline estimate for sharing the same date of birth wo |
7,553 | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner? | The chances for this to happen.... two people having their birthday on the same day as explained by the other posters is 1/365 * 1/30 to be conservative here with the age ranges. To be in a relationship, a successful one multiply by maybe 1/2 or 1/3?!
However, for you to be in a relationship, you first have to be here... | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner? | The chances for this to happen.... two people having their birthday on the same day as explained by the other posters is 1/365 * 1/30 to be conservative here with the age ranges. To be in a relationsh | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner?
The chances for this to happen.... two people having their birthday on the same day as explained by the other posters is 1/365 * 1/30 to be conservative here with the age ranges. To be in a relationship, a successful one... | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner?
The chances for this to happen.... two people having their birthday on the same day as explained by the other posters is 1/365 * 1/30 to be conservative here with the age ranges. To be in a relationsh |
7,554 | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner? | Although the question is about birthdays, the "birthday paradox" isn't really relevant here. It's about how many random samples you need to take before you expect at least two samples among them to be equal (a collision). Your question is mostly about the probability of two samples being equal. If there were 30 people ... | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner? | Although the question is about birthdays, the "birthday paradox" isn't really relevant here. It's about how many random samples you need to take before you expect at least two samples among them to be | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner?
Although the question is about birthdays, the "birthday paradox" isn't really relevant here. It's about how many random samples you need to take before you expect at least two samples among them to be equal (a collision)... | Birthday paradox with a (huge) twist: Probability of sharing exact same date of birth with partner?
Although the question is about birthdays, the "birthday paradox" isn't really relevant here. It's about how many random samples you need to take before you expect at least two samples among them to be |
7,555 | Why do some people use -999 or -9999 to replace missing values? | This is a holdout from earlier times, when computer software stored numerical vectors as numerical vectors. No real number has the semantics "I'm missing". So when early statistical software had to differentiate between "true" numbers and missing values, they put in something that was "obviously" not a valid number, li... | Why do some people use -999 or -9999 to replace missing values? | This is a holdout from earlier times, when computer software stored numerical vectors as numerical vectors. No real number has the semantics "I'm missing". So when early statistical software had to di | Why do some people use -999 or -9999 to replace missing values?
This is a holdout from earlier times, when computer software stored numerical vectors as numerical vectors. No real number has the semantics "I'm missing". So when early statistical software had to differentiate between "true" numbers and missing values, t... | Why do some people use -999 or -9999 to replace missing values?
This is a holdout from earlier times, when computer software stored numerical vectors as numerical vectors. No real number has the semantics "I'm missing". So when early statistical software had to di |
7,556 | Why do some people use -999 or -9999 to replace missing values? | Such values are for databases. Most databases long ago, and many today, allocated a fixed number of digits for integer-valued data. A number like -999 is the smallest that can be stored in four characters, -9999 in five characters, and so on.
(It should go without saying that--by definition--a numeric field cannot st... | Why do some people use -999 or -9999 to replace missing values? | Such values are for databases. Most databases long ago, and many today, allocated a fixed number of digits for integer-valued data. A number like -999 is the smallest that can be stored in four char | Why do some people use -999 or -9999 to replace missing values?
Such values are for databases. Most databases long ago, and many today, allocated a fixed number of digits for integer-valued data. A number like -999 is the smallest that can be stored in four characters, -9999 in five characters, and so on.
(It should ... | Why do some people use -999 or -9999 to replace missing values?
Such values are for databases. Most databases long ago, and many today, allocated a fixed number of digits for integer-valued data. A number like -999 is the smallest that can be stored in four char |
7,557 | Why do some people use -999 or -9999 to replace missing values? | You can use anything to encode missing values. Some software, like R, use special values to encode missing data, but there are also software packages, e.g. SPSS, that do not have any special codes for missing data. In the second case you need to make arbitrary choice for such values. You can choose anything, but genera... | Why do some people use -999 or -9999 to replace missing values? | You can use anything to encode missing values. Some software, like R, use special values to encode missing data, but there are also software packages, e.g. SPSS, that do not have any special codes for | Why do some people use -999 or -9999 to replace missing values?
You can use anything to encode missing values. Some software, like R, use special values to encode missing data, but there are also software packages, e.g. SPSS, that do not have any special codes for missing data. In the second case you need to make arbit... | Why do some people use -999 or -9999 to replace missing values?
You can use anything to encode missing values. Some software, like R, use special values to encode missing data, but there are also software packages, e.g. SPSS, that do not have any special codes for |
7,558 | Why do some people use -999 or -9999 to replace missing values? | Are there computed variables in the dataset? Or is this an analytic dataset that comes form merged / sorted data? Some software uses very large negative values to denote missing data. But other software creates missing values with NA or .. When they are discrepant, usually some post processing has led to disagreement. | Why do some people use -999 or -9999 to replace missing values? | Are there computed variables in the dataset? Or is this an analytic dataset that comes form merged / sorted data? Some software uses very large negative values to denote missing data. But other softwa | Why do some people use -999 or -9999 to replace missing values?
Are there computed variables in the dataset? Or is this an analytic dataset that comes form merged / sorted data? Some software uses very large negative values to denote missing data. But other software creates missing values with NA or .. When they are di... | Why do some people use -999 or -9999 to replace missing values?
Are there computed variables in the dataset? Or is this an analytic dataset that comes form merged / sorted data? Some software uses very large negative values to denote missing data. But other softwa |
7,559 | Why do some people use -999 or -9999 to replace missing values? | Of course, in SPSS, the missing value(s) 999 or whatever IS tagged as a special missing code and handled separately from other values. It may be tabulated separately or excluded entirely. A distinction is made from the result of things like zero division or log(0). | Why do some people use -999 or -9999 to replace missing values? | Of course, in SPSS, the missing value(s) 999 or whatever IS tagged as a special missing code and handled separately from other values. It may be tabulated separately or excluded entirely. A distinct | Why do some people use -999 or -9999 to replace missing values?
Of course, in SPSS, the missing value(s) 999 or whatever IS tagged as a special missing code and handled separately from other values. It may be tabulated separately or excluded entirely. A distinction is made from the result of things like zero division... | Why do some people use -999 or -9999 to replace missing values?
Of course, in SPSS, the missing value(s) 999 or whatever IS tagged as a special missing code and handled separately from other values. It may be tabulated separately or excluded entirely. A distinct |
7,560 | Why is generating 8 random bits uniform on (0, 255)? | TL;DR:
The sharp contrast between the bits and coins is that in the case of the coins, you're ignoring the order of the outcomes. HHHHTTTT is treated as the same as TTTTHHHH (both have 4 heads and 4 tails). But in bits, you care about the order (because you have to give "weights" to the bit positions in order to get 25... | Why is generating 8 random bits uniform on (0, 255)? | TL;DR:
The sharp contrast between the bits and coins is that in the case of the coins, you're ignoring the order of the outcomes. HHHHTTTT is treated as the same as TTTTHHHH (both have 4 heads and 4 t | Why is generating 8 random bits uniform on (0, 255)?
TL;DR:
The sharp contrast between the bits and coins is that in the case of the coins, you're ignoring the order of the outcomes. HHHHTTTT is treated as the same as TTTTHHHH (both have 4 heads and 4 tails). But in bits, you care about the order (because you have to g... | Why is generating 8 random bits uniform on (0, 255)?
TL;DR:
The sharp contrast between the bits and coins is that in the case of the coins, you're ignoring the order of the outcomes. HHHHTTTT is treated as the same as TTTTHHHH (both have 4 heads and 4 t |
7,561 | Why is generating 8 random bits uniform on (0, 255)? | why does a sequence of 8 zeroes or 8 ones seem to be equally as likely as a sequence of 4 and 4, or 5 and 3, etc
The aparent paradox can be summarized in two propositions, that might seem contradictory:
The sequence $s_1: 00000000$ (eight zeroes) is equally probable as sequence $s_2: 01010101$ (four zeroes, four one... | Why is generating 8 random bits uniform on (0, 255)? | why does a sequence of 8 zeroes or 8 ones seem to be equally as likely as a sequence of 4 and 4, or 5 and 3, etc
The aparent paradox can be summarized in two propositions, that might seem contradicto | Why is generating 8 random bits uniform on (0, 255)?
why does a sequence of 8 zeroes or 8 ones seem to be equally as likely as a sequence of 4 and 4, or 5 and 3, etc
The aparent paradox can be summarized in two propositions, that might seem contradictory:
The sequence $s_1: 00000000$ (eight zeroes) is equally probab... | Why is generating 8 random bits uniform on (0, 255)?
why does a sequence of 8 zeroes or 8 ones seem to be equally as likely as a sequence of 4 and 4, or 5 and 3, etc
The aparent paradox can be summarized in two propositions, that might seem contradicto |
7,562 | Why is generating 8 random bits uniform on (0, 255)? | All of the $2^8$ sequences have the same probability 1/$2^8$=1/256. It is a wrong to think that the sequences that have closer to an equal number of 0s and 1s is more likely as the question is interpreted.. It should be clear that we arrive at 1/256 because we assume independence from trial to trial. That is why we mul... | Why is generating 8 random bits uniform on (0, 255)? | All of the $2^8$ sequences have the same probability 1/$2^8$=1/256. It is a wrong to think that the sequences that have closer to an equal number of 0s and 1s is more likely as the question is interpr | Why is generating 8 random bits uniform on (0, 255)?
All of the $2^8$ sequences have the same probability 1/$2^8$=1/256. It is a wrong to think that the sequences that have closer to an equal number of 0s and 1s is more likely as the question is interpreted.. It should be clear that we arrive at 1/256 because we assume... | Why is generating 8 random bits uniform on (0, 255)?
All of the $2^8$ sequences have the same probability 1/$2^8$=1/256. It is a wrong to think that the sequences that have closer to an equal number of 0s and 1s is more likely as the question is interpr |
7,563 | Why is generating 8 random bits uniform on (0, 255)? | EXAMPLE with 3 bits (often an example is more illustrative)
I will write the natural numbers 0 through 7 as:
A number in base 10
A number in base 2 (i.e. a sequence of bits)
A series of coin flips implied by the base 2 representation (1 denotes a flip of heads and 0 denotes a flip of tails).
$$\begin{array}
&\text{Ba... | Why is generating 8 random bits uniform on (0, 255)? | EXAMPLE with 3 bits (often an example is more illustrative)
I will write the natural numbers 0 through 7 as:
A number in base 10
A number in base 2 (i.e. a sequence of bits)
A series of coin flips im | Why is generating 8 random bits uniform on (0, 255)?
EXAMPLE with 3 bits (often an example is more illustrative)
I will write the natural numbers 0 through 7 as:
A number in base 10
A number in base 2 (i.e. a sequence of bits)
A series of coin flips implied by the base 2 representation (1 denotes a flip of heads and 0... | Why is generating 8 random bits uniform on (0, 255)?
EXAMPLE with 3 bits (often an example is more illustrative)
I will write the natural numbers 0 through 7 as:
A number in base 10
A number in base 2 (i.e. a sequence of bits)
A series of coin flips im |
7,564 | Why is generating 8 random bits uniform on (0, 255)? | Sycorax's answer is correct, but it seems like you're not entirely clear on why.
When you flip 8 coins or generate 8 random bits taking order into account, your result will be one of 256 equally likely possibilities. In your case, each of these 256 possible outcomes uniquely map to an integer, so you get a uniform dist... | Why is generating 8 random bits uniform on (0, 255)? | Sycorax's answer is correct, but it seems like you're not entirely clear on why.
When you flip 8 coins or generate 8 random bits taking order into account, your result will be one of 256 equally likel | Why is generating 8 random bits uniform on (0, 255)?
Sycorax's answer is correct, but it seems like you're not entirely clear on why.
When you flip 8 coins or generate 8 random bits taking order into account, your result will be one of 256 equally likely possibilities. In your case, each of these 256 possible outcomes ... | Why is generating 8 random bits uniform on (0, 255)?
Sycorax's answer is correct, but it seems like you're not entirely clear on why.
When you flip 8 coins or generate 8 random bits taking order into account, your result will be one of 256 equally likel |
7,565 | Why is generating 8 random bits uniform on (0, 255)? | The problem, restated, is: Why is the number of combinations of 8 random binary digits taken as 0 to 8 selected digits (e.g., the 1's) at a time different from the number of permutations of 8 random binary digits. In the context herein, random choice of 0's and 1's means that each digit is independent of any other, so ... | Why is generating 8 random bits uniform on (0, 255)? | The problem, restated, is: Why is the number of combinations of 8 random binary digits taken as 0 to 8 selected digits (e.g., the 1's) at a time different from the number of permutations of 8 random b | Why is generating 8 random bits uniform on (0, 255)?
The problem, restated, is: Why is the number of combinations of 8 random binary digits taken as 0 to 8 selected digits (e.g., the 1's) at a time different from the number of permutations of 8 random binary digits. In the context herein, random choice of 0's and 1's m... | Why is generating 8 random bits uniform on (0, 255)?
The problem, restated, is: Why is the number of combinations of 8 random binary digits taken as 0 to 8 selected digits (e.g., the 1's) at a time different from the number of permutations of 8 random b |
7,566 | Why is generating 8 random bits uniform on (0, 255)? | Each bit you choose is independent from each other bit. If you consider for the first bit there is a
50% probability it will be 1
and
50% probability it will be 0.
This also applies to the second bit, third bit and so on so that you end up with so for each possible combination of bits to make your byte you have ... | Why is generating 8 random bits uniform on (0, 255)? | Each bit you choose is independent from each other bit. If you consider for the first bit there is a
50% probability it will be 1
and
50% probability it will be 0.
This also applies to the seco | Why is generating 8 random bits uniform on (0, 255)?
Each bit you choose is independent from each other bit. If you consider for the first bit there is a
50% probability it will be 1
and
50% probability it will be 0.
This also applies to the second bit, third bit and so on so that you end up with so for each pos... | Why is generating 8 random bits uniform on (0, 255)?
Each bit you choose is independent from each other bit. If you consider for the first bit there is a
50% probability it will be 1
and
50% probability it will be 0.
This also applies to the seco |
7,567 | Why is generating 8 random bits uniform on (0, 255)? | If you do a binary search comparing each bit, then you need the same number of steps for each 8 bit number, from 0000 0000 to 1111 1111, they both have the length 8 bit. At each step in the binary search both sides have a 50/50 chance of occuring, so in the end, because every number has the same depth and the same prob... | Why is generating 8 random bits uniform on (0, 255)? | If you do a binary search comparing each bit, then you need the same number of steps for each 8 bit number, from 0000 0000 to 1111 1111, they both have the length 8 bit. At each step in the binary sea | Why is generating 8 random bits uniform on (0, 255)?
If you do a binary search comparing each bit, then you need the same number of steps for each 8 bit number, from 0000 0000 to 1111 1111, they both have the length 8 bit. At each step in the binary search both sides have a 50/50 chance of occuring, so in the end, beca... | Why is generating 8 random bits uniform on (0, 255)?
If you do a binary search comparing each bit, then you need the same number of steps for each 8 bit number, from 0000 0000 to 1111 1111, they both have the length 8 bit. At each step in the binary sea |
7,568 | Why is generating 8 random bits uniform on (0, 255)? | There is only one sequence with eight zeros. There are seventy sequences with four zeros and four ones.
Therefore, while 0 has a probability of 0.39%, and 15 [00001111] also has a probability of 0.39%, and 23 [00010111] has a probability of 0.39%, etc., if you add up all seventy of the 0.39% probabilities you get 27.3%... | Why is generating 8 random bits uniform on (0, 255)? | There is only one sequence with eight zeros. There are seventy sequences with four zeros and four ones.
Therefore, while 0 has a probability of 0.39%, and 15 [00001111] also has a probability of 0.39% | Why is generating 8 random bits uniform on (0, 255)?
There is only one sequence with eight zeros. There are seventy sequences with four zeros and four ones.
Therefore, while 0 has a probability of 0.39%, and 15 [00001111] also has a probability of 0.39%, and 23 [00010111] has a probability of 0.39%, etc., if you add up... | Why is generating 8 random bits uniform on (0, 255)?
There is only one sequence with eight zeros. There are seventy sequences with four zeros and four ones.
Therefore, while 0 has a probability of 0.39%, and 15 [00001111] also has a probability of 0.39% |
7,569 | Why is generating 8 random bits uniform on (0, 255)? | Consider dice
Think about rolling a couple of dice, a common example of non-uniform distribution. For the sake of the math, imagine the dice are numbered from 0 to 5 instead of the traditional 1 to 6. The reason the distribution is not uniform is that you are looking at the sum of the dice rolls, where multiple combina... | Why is generating 8 random bits uniform on (0, 255)? | Consider dice
Think about rolling a couple of dice, a common example of non-uniform distribution. For the sake of the math, imagine the dice are numbered from 0 to 5 instead of the traditional 1 to 6. | Why is generating 8 random bits uniform on (0, 255)?
Consider dice
Think about rolling a couple of dice, a common example of non-uniform distribution. For the sake of the math, imagine the dice are numbered from 0 to 5 instead of the traditional 1 to 6. The reason the distribution is not uniform is that you are looking... | Why is generating 8 random bits uniform on (0, 255)?
Consider dice
Think about rolling a couple of dice, a common example of non-uniform distribution. For the sake of the math, imagine the dice are numbered from 0 to 5 instead of the traditional 1 to 6. |
7,570 | Why is generating 8 random bits uniform on (0, 255)? | I'd like to expand a little bit on the idea of order dependence vs. independence.
In the problem of calculating the expected number of heads from flipping 8 coins, we're summing the values from 8 identical distributions, each of which is the Bernoulli distribution $ B(1, 0.5) $ (in other words, a 50% chance of 0, a 50%... | Why is generating 8 random bits uniform on (0, 255)? | I'd like to expand a little bit on the idea of order dependence vs. independence.
In the problem of calculating the expected number of heads from flipping 8 coins, we're summing the values from 8 iden | Why is generating 8 random bits uniform on (0, 255)?
I'd like to expand a little bit on the idea of order dependence vs. independence.
In the problem of calculating the expected number of heads from flipping 8 coins, we're summing the values from 8 identical distributions, each of which is the Bernoulli distribution $ ... | Why is generating 8 random bits uniform on (0, 255)?
I'd like to expand a little bit on the idea of order dependence vs. independence.
In the problem of calculating the expected number of heads from flipping 8 coins, we're summing the values from 8 iden |
7,571 | Is an overfitted model necessarily useless? | I think the argument is correct. If 70% is acceptable in the particular application, then the model is useful even though it is overfitted (more generally, regardless of whether it is overfitted or not).
While balancing overfitting against underfitting concerns optimality (looking for an optimal solution), having sat... | Is an overfitted model necessarily useless? | I think the argument is correct. If 70% is acceptable in the particular application, then the model is useful even though it is overfitted (more generally, regardless of whether it is overfitted or no | Is an overfitted model necessarily useless?
I think the argument is correct. If 70% is acceptable in the particular application, then the model is useful even though it is overfitted (more generally, regardless of whether it is overfitted or not).
While balancing overfitting against underfitting concerns optimality (... | Is an overfitted model necessarily useless?
I think the argument is correct. If 70% is acceptable in the particular application, then the model is useful even though it is overfitted (more generally, regardless of whether it is overfitted or no |
7,572 | Is an overfitted model necessarily useless? | In my past project with Credit Card Fraud detection, we intentionally want to over fit the data / hard coded to remember fraud cases. (Note, overfitting one class is not exactly the general overfitting problem OP talked about.) Such system has relatively low false positives and satisfy our needs.
So, I would say, overf... | Is an overfitted model necessarily useless? | In my past project with Credit Card Fraud detection, we intentionally want to over fit the data / hard coded to remember fraud cases. (Note, overfitting one class is not exactly the general overfittin | Is an overfitted model necessarily useless?
In my past project with Credit Card Fraud detection, we intentionally want to over fit the data / hard coded to remember fraud cases. (Note, overfitting one class is not exactly the general overfitting problem OP talked about.) Such system has relatively low false positives a... | Is an overfitted model necessarily useless?
In my past project with Credit Card Fraud detection, we intentionally want to over fit the data / hard coded to remember fraud cases. (Note, overfitting one class is not exactly the general overfittin |
7,573 | Is an overfitted model necessarily useless? | Maybe: beware. When you say that 70% accuracy (however you measure it) is good enough for you, it feels like you're assuming that errors are randomly or evenly distributed.
But one of the ways of looking at overfitting is that it happens when a model technique allows (and its training process encourages) paying too muc... | Is an overfitted model necessarily useless? | Maybe: beware. When you say that 70% accuracy (however you measure it) is good enough for you, it feels like you're assuming that errors are randomly or evenly distributed.
But one of the ways of look | Is an overfitted model necessarily useless?
Maybe: beware. When you say that 70% accuracy (however you measure it) is good enough for you, it feels like you're assuming that errors are randomly or evenly distributed.
But one of the ways of looking at overfitting is that it happens when a model technique allows (and its... | Is an overfitted model necessarily useless?
Maybe: beware. When you say that 70% accuracy (however you measure it) is good enough for you, it feels like you're assuming that errors are randomly or evenly distributed.
But one of the ways of look |
7,574 | Is an overfitted model necessarily useless? | No they can be useful, but it depends on your purpose. Several things spring to mind:
Cost-Sensitive Classification: If your evaluation function overweights TPR and underweights FPR, we use $F_\beta$ score with $\beta \gg 1$. (such as @hxd1011's answer on antifraud)
Such a classifier can be really useful in an ensembl... | Is an overfitted model necessarily useless? | No they can be useful, but it depends on your purpose. Several things spring to mind:
Cost-Sensitive Classification: If your evaluation function overweights TPR and underweights FPR, we use $F_\beta$ | Is an overfitted model necessarily useless?
No they can be useful, but it depends on your purpose. Several things spring to mind:
Cost-Sensitive Classification: If your evaluation function overweights TPR and underweights FPR, we use $F_\beta$ score with $\beta \gg 1$. (such as @hxd1011's answer on antifraud)
Such a c... | Is an overfitted model necessarily useless?
No they can be useful, but it depends on your purpose. Several things spring to mind:
Cost-Sensitive Classification: If your evaluation function overweights TPR and underweights FPR, we use $F_\beta$ |
7,575 | Is an overfitted model necessarily useless? | I'm not denying that an overfitted model could still be useful. But just keep in mind that this 70% could be a misleading information. What you need in order to judge if a model is useful or not is the out-of-sample error, not the testing error (the out-of-sample error is not known, so we have to estimate it using a bl... | Is an overfitted model necessarily useless? | I'm not denying that an overfitted model could still be useful. But just keep in mind that this 70% could be a misleading information. What you need in order to judge if a model is useful or not is th | Is an overfitted model necessarily useless?
I'm not denying that an overfitted model could still be useful. But just keep in mind that this 70% could be a misleading information. What you need in order to judge if a model is useful or not is the out-of-sample error, not the testing error (the out-of-sample error is not... | Is an overfitted model necessarily useless?
I'm not denying that an overfitted model could still be useful. But just keep in mind that this 70% could be a misleading information. What you need in order to judge if a model is useful or not is th |
7,576 | Extrapolation v. Interpolation | To add a visual explanation to this: let's consider a few points that you plan to model.
They look like they could be described well with a straight line, so you fit a linear regression to them:
This regression line lets you both interpolate (generate expected values in between your data points) and extrapolate (ge... | Extrapolation v. Interpolation | To add a visual explanation to this: let's consider a few points that you plan to model.
They look like they could be described well with a straight line, so you fit a linear regression to them:
T | Extrapolation v. Interpolation
To add a visual explanation to this: let's consider a few points that you plan to model.
They look like they could be described well with a straight line, so you fit a linear regression to them:
This regression line lets you both interpolate (generate expected values in between your d... | Extrapolation v. Interpolation
To add a visual explanation to this: let's consider a few points that you plan to model.
They look like they could be described well with a straight line, so you fit a linear regression to them:
T |
7,577 | Extrapolation v. Interpolation | In essence interpolation is an operation within the data support, or between existing known data points; extrapolation is beyond the data support. Otherwise put, the criterion is: where are the missing values?
One reason for the distinction is that extrapolation is usually more difficult to do well, and even dangerou... | Extrapolation v. Interpolation | In essence interpolation is an operation within the data support, or between existing known data points; extrapolation is beyond the data support. Otherwise put, the criterion is: where are the missin | Extrapolation v. Interpolation
In essence interpolation is an operation within the data support, or between existing known data points; extrapolation is beyond the data support. Otherwise put, the criterion is: where are the missing values?
One reason for the distinction is that extrapolation is usually more difficul... | Extrapolation v. Interpolation
In essence interpolation is an operation within the data support, or between existing known data points; extrapolation is beyond the data support. Otherwise put, the criterion is: where are the missin |
7,578 | Extrapolation v. Interpolation | TL;DR version:
Interpolation takes place between existing data points.
Extrapolation takes place beyond them.
Mnemonic: interpolation => inside.
FWIW: The prefix inter- means between, and extra- means beyond. Think also of interstate highways which go between states, or extraterrestrials from beyond our planet. | Extrapolation v. Interpolation | TL;DR version:
Interpolation takes place between existing data points.
Extrapolation takes place beyond them.
Mnemonic: interpolation => inside.
FWIW: The prefix inter- means between, and extra- | Extrapolation v. Interpolation
TL;DR version:
Interpolation takes place between existing data points.
Extrapolation takes place beyond them.
Mnemonic: interpolation => inside.
FWIW: The prefix inter- means between, and extra- means beyond. Think also of interstate highways which go between states, or extraterres... | Extrapolation v. Interpolation
TL;DR version:
Interpolation takes place between existing data points.
Extrapolation takes place beyond them.
Mnemonic: interpolation => inside.
FWIW: The prefix inter- means between, and extra- |
7,579 | Extrapolation v. Interpolation | Example:
Study: Want to fit a simple linear regression on the height on the age for girls of age 6-15 years old. Sample size is 100, age is calculated by (date of measuring - date of birth)/365.25.
After data collection, model is fit and get the estimate of intercept b0 and slope b1. it means we have E(height|age) = ... | Extrapolation v. Interpolation | Example:
Study: Want to fit a simple linear regression on the height on the age for girls of age 6-15 years old. Sample size is 100, age is calculated by (date of measuring - date of birth)/365.25.
| Extrapolation v. Interpolation
Example:
Study: Want to fit a simple linear regression on the height on the age for girls of age 6-15 years old. Sample size is 100, age is calculated by (date of measuring - date of birth)/365.25.
After data collection, model is fit and get the estimate of intercept b0 and slope b1. it... | Extrapolation v. Interpolation
Example:
Study: Want to fit a simple linear regression on the height on the age for girls of age 6-15 years old. Sample size is 100, age is calculated by (date of measuring - date of birth)/365.25.
|
7,580 | Extrapolation v. Interpolation | The extrapolation v.s. interpolation also applys in neural networks as mentioned in Rethinking Eliminative Connectionism and Deep Learning: A Critical Appraisal:
generalization can be thought of as coming in two flavors,
interpolation between known examples, and extrapolation, which
requires going beyond a space of kn... | Extrapolation v. Interpolation | The extrapolation v.s. interpolation also applys in neural networks as mentioned in Rethinking Eliminative Connectionism and Deep Learning: A Critical Appraisal:
generalization can be thought of as c | Extrapolation v. Interpolation
The extrapolation v.s. interpolation also applys in neural networks as mentioned in Rethinking Eliminative Connectionism and Deep Learning: A Critical Appraisal:
generalization can be thought of as coming in two flavors,
interpolation between known examples, and extrapolation, which
requ... | Extrapolation v. Interpolation
The extrapolation v.s. interpolation also applys in neural networks as mentioned in Rethinking Eliminative Connectionism and Deep Learning: A Critical Appraisal:
generalization can be thought of as c |
7,581 | Statistical methods to more efficiently plot data when millions of points are present? | This is a hard task with no ready solutions (this is of course because density plot is so a tempting fallback than no one really cares). So, what can you do?
If they really overlap (i.e. have exactly the same X & Y coordinates) and you are not using alpha, the best idea would be just to reduce the overlap using unique ... | Statistical methods to more efficiently plot data when millions of points are present? | This is a hard task with no ready solutions (this is of course because density plot is so a tempting fallback than no one really cares). So, what can you do?
If they really overlap (i.e. have exactly | Statistical methods to more efficiently plot data when millions of points are present?
This is a hard task with no ready solutions (this is of course because density plot is so a tempting fallback than no one really cares). So, what can you do?
If they really overlap (i.e. have exactly the same X & Y coordinates) and y... | Statistical methods to more efficiently plot data when millions of points are present?
This is a hard task with no ready solutions (this is of course because density plot is so a tempting fallback than no one really cares). So, what can you do?
If they really overlap (i.e. have exactly |
7,582 | Statistical methods to more efficiently plot data when millions of points are present? | Look at the hexbin package which implements paper/method by Dan Carr. The pdf vignette has more details which I quote below:
1 Overview
Hexagon binning is a form of bivariate
histogram useful for visualizing the
struc- ture in datasets with large n.
The underlying concept of hexagon
binning is extremely simple... | Statistical methods to more efficiently plot data when millions of points are present? | Look at the hexbin package which implements paper/method by Dan Carr. The pdf vignette has more details which I quote below:
1 Overview
Hexagon binning is a form of bivariate
histogram useful for v | Statistical methods to more efficiently plot data when millions of points are present?
Look at the hexbin package which implements paper/method by Dan Carr. The pdf vignette has more details which I quote below:
1 Overview
Hexagon binning is a form of bivariate
histogram useful for visualizing the
struc- ture in d... | Statistical methods to more efficiently plot data when millions of points are present?
Look at the hexbin package which implements paper/method by Dan Carr. The pdf vignette has more details which I quote below:
1 Overview
Hexagon binning is a form of bivariate
histogram useful for v |
7,583 | Statistical methods to more efficiently plot data when millions of points are present? | I must admit that I do not fully understand your last paragraph:
"I am not looking for a density plot
(although those are often useful), I
would want the same output as a simple
plot call but much faster than
millions of overplots if possible."
It is also unclear what type of plot (function) you are looking f... | Statistical methods to more efficiently plot data when millions of points are present? | I must admit that I do not fully understand your last paragraph:
"I am not looking for a density plot
(although those are often useful), I
would want the same output as a simple
plot call but m | Statistical methods to more efficiently plot data when millions of points are present?
I must admit that I do not fully understand your last paragraph:
"I am not looking for a density plot
(although those are often useful), I
would want the same output as a simple
plot call but much faster than
millions of ove... | Statistical methods to more efficiently plot data when millions of points are present?
I must admit that I do not fully understand your last paragraph:
"I am not looking for a density plot
(although those are often useful), I
would want the same output as a simple
plot call but m |
7,584 | Statistical methods to more efficiently plot data when millions of points are present? | Another direct answer to the question is the rgl package, which can plot millions of points using OpenGL. Also, specify a point size (e.g. 3) and zoom out to see these centers of masses as monolithic blocks, or zoom in and see the structure of what used to be monolithic - the point sizes are constant but the distances ... | Statistical methods to more efficiently plot data when millions of points are present? | Another direct answer to the question is the rgl package, which can plot millions of points using OpenGL. Also, specify a point size (e.g. 3) and zoom out to see these centers of masses as monolithic | Statistical methods to more efficiently plot data when millions of points are present?
Another direct answer to the question is the rgl package, which can plot millions of points using OpenGL. Also, specify a point size (e.g. 3) and zoom out to see these centers of masses as monolithic blocks, or zoom in and see the st... | Statistical methods to more efficiently plot data when millions of points are present?
Another direct answer to the question is the rgl package, which can plot millions of points using OpenGL. Also, specify a point size (e.g. 3) and zoom out to see these centers of masses as monolithic |
7,585 | Statistical methods to more efficiently plot data when millions of points are present? | Here's a file I call bigplotfix.R. If you source it, it will define a wrapper for plot.xy which "compresses" the plot data when it is very large. The wrapper does nothing if the input is small, but if the input is large then it breaks it into chunks and just plots the maximum and minimum x and y value for each chunk. S... | Statistical methods to more efficiently plot data when millions of points are present? | Here's a file I call bigplotfix.R. If you source it, it will define a wrapper for plot.xy which "compresses" the plot data when it is very large. The wrapper does nothing if the input is small, but if | Statistical methods to more efficiently plot data when millions of points are present?
Here's a file I call bigplotfix.R. If you source it, it will define a wrapper for plot.xy which "compresses" the plot data when it is very large. The wrapper does nothing if the input is small, but if the input is large then it break... | Statistical methods to more efficiently plot data when millions of points are present?
Here's a file I call bigplotfix.R. If you source it, it will define a wrapper for plot.xy which "compresses" the plot data when it is very large. The wrapper does nothing if the input is small, but if |
7,586 | Statistical methods to more efficiently plot data when millions of points are present? | For large time series, I have grown to love smoothScatter (part of base R no less). I often have to include some additional data, and preserving the basic plot API is really helpful, for instance:
set.seed(1)
ra <- rnorm(n = 100000, sd = 1, mean = 0)
smoothScatter(ra)
abline(v=25000, col=2)
text(25000, 0, "Event 1", co... | Statistical methods to more efficiently plot data when millions of points are present? | For large time series, I have grown to love smoothScatter (part of base R no less). I often have to include some additional data, and preserving the basic plot API is really helpful, for instance:
set | Statistical methods to more efficiently plot data when millions of points are present?
For large time series, I have grown to love smoothScatter (part of base R no less). I often have to include some additional data, and preserving the basic plot API is really helpful, for instance:
set.seed(1)
ra <- rnorm(n = 100000, ... | Statistical methods to more efficiently plot data when millions of points are present?
For large time series, I have grown to love smoothScatter (part of base R no less). I often have to include some additional data, and preserving the basic plot API is really helpful, for instance:
set |
7,587 | Statistical methods to more efficiently plot data when millions of points are present? | Maybe I'll get shunned for my method, bad memories of one of my research profs screaming at people for throwing away good data by translating them into categories (of course, I do agree now a days lol), don't know. Anyway, if you're talking about a scatterplot, then I've had the same issues. Now, when I have numeric... | Statistical methods to more efficiently plot data when millions of points are present? | Maybe I'll get shunned for my method, bad memories of one of my research profs screaming at people for throwing away good data by translating them into categories (of course, I do agree now a days lol | Statistical methods to more efficiently plot data when millions of points are present?
Maybe I'll get shunned for my method, bad memories of one of my research profs screaming at people for throwing away good data by translating them into categories (of course, I do agree now a days lol), don't know. Anyway, if you're... | Statistical methods to more efficiently plot data when millions of points are present?
Maybe I'll get shunned for my method, bad memories of one of my research profs screaming at people for throwing away good data by translating them into categories (of course, I do agree now a days lol |
7,588 | Likelihood - Why multiply? | This is a very basic question, and instead of using formal language and mathematical notation, I will try to answer it at a level at which everybody who can understand the question can also understand the answer.
Imagine that we have a race of cats. They have a 75% probability of being born white, and 25% probability ... | Likelihood - Why multiply? | This is a very basic question, and instead of using formal language and mathematical notation, I will try to answer it at a level at which everybody who can understand the question can also understand | Likelihood - Why multiply?
This is a very basic question, and instead of using formal language and mathematical notation, I will try to answer it at a level at which everybody who can understand the question can also understand the answer.
Imagine that we have a race of cats. They have a 75% probability of being born ... | Likelihood - Why multiply?
This is a very basic question, and instead of using formal language and mathematical notation, I will try to answer it at a level at which everybody who can understand the question can also understand |
7,589 | Likelihood - Why multiply? | Independence between two events means that the occurrence of one event does not affect the likelihood of the occurrence of the another event . So for any two events $A$ and $B$ in a sample space $S$ we say that $A$ and $B$ are independent iff $P(A$ and $B)=P(A\cap B) = P(A)P(B)$ .Now for more than two events we say t... | Likelihood - Why multiply? | Independence between two events means that the occurrence of one event does not affect the likelihood of the occurrence of the another event . So for any two events $A$ and $B$ in a sample space $S$ | Likelihood - Why multiply?
Independence between two events means that the occurrence of one event does not affect the likelihood of the occurrence of the another event . So for any two events $A$ and $B$ in a sample space $S$ we say that $A$ and $B$ are independent iff $P(A$ and $B)=P(A\cap B) = P(A)P(B)$ .Now for mo... | Likelihood - Why multiply?
Independence between two events means that the occurrence of one event does not affect the likelihood of the occurrence of the another event . So for any two events $A$ and $B$ in a sample space $S$ |
7,590 | Likelihood - Why multiply? | Under the common assumption of independence, $P(A \cap B)$ = $P(A) P(B)$.
Thus, if you assume that all your observations are independent, then the probability of observing all the values you saw is equal to the product of the individual probabilities. | Likelihood - Why multiply? | Under the common assumption of independence, $P(A \cap B)$ = $P(A) P(B)$.
Thus, if you assume that all your observations are independent, then the probability of observing all the values you saw is e | Likelihood - Why multiply?
Under the common assumption of independence, $P(A \cap B)$ = $P(A) P(B)$.
Thus, if you assume that all your observations are independent, then the probability of observing all the values you saw is equal to the product of the individual probabilities. | Likelihood - Why multiply?
Under the common assumption of independence, $P(A \cap B)$ = $P(A) P(B)$.
Thus, if you assume that all your observations are independent, then the probability of observing all the values you saw is e |
7,591 | Likelihood - Why multiply? | Why not add?
Because that clearly makes no sense. Suppose you have a quarter and a nickel, and you want to flip them both. There's a 50% chance the quarter will come up heads, and a 50% chance the nickel comes up heads. If the chance of both coming up heads were the sum, that would make 100% chance, which is obviousl... | Likelihood - Why multiply? | Why not add?
Because that clearly makes no sense. Suppose you have a quarter and a nickel, and you want to flip them both. There's a 50% chance the quarter will come up heads, and a 50% chance the ni | Likelihood - Why multiply?
Why not add?
Because that clearly makes no sense. Suppose you have a quarter and a nickel, and you want to flip them both. There's a 50% chance the quarter will come up heads, and a 50% chance the nickel comes up heads. If the chance of both coming up heads were the sum, that would make 100... | Likelihood - Why multiply?
Why not add?
Because that clearly makes no sense. Suppose you have a quarter and a nickel, and you want to flip them both. There's a 50% chance the quarter will come up heads, and a 50% chance the ni |
7,592 | Likelihood - Why multiply? | I am reading these posts because, like the Original Poster, my need is to understand why the 'Likelihood' fn is the 'Product' of the density of each sample value -'x'.
A readable and logical reason is given under the heading Principle of maximum likelihood Ref:
http://www-structmed.cimr.cam.ac.uk/Course/Likelihood/like... | Likelihood - Why multiply? | I am reading these posts because, like the Original Poster, my need is to understand why the 'Likelihood' fn is the 'Product' of the density of each sample value -'x'.
A readable and logical reason is | Likelihood - Why multiply?
I am reading these posts because, like the Original Poster, my need is to understand why the 'Likelihood' fn is the 'Product' of the density of each sample value -'x'.
A readable and logical reason is given under the heading Principle of maximum likelihood Ref:
http://www-structmed.cimr.cam.a... | Likelihood - Why multiply?
I am reading these posts because, like the Original Poster, my need is to understand why the 'Likelihood' fn is the 'Product' of the density of each sample value -'x'.
A readable and logical reason is |
7,593 | Likelihood - Why multiply? | The goal of the maximum likelihood method is find estimator that maximize the probability of observe certains values of the variable ( endogenous variable). That is the reason why we must multiply the probabilties of ocurrence.
For example: imagine that the numbers of phone calls that a secretary can answer in an hour... | Likelihood - Why multiply? | The goal of the maximum likelihood method is find estimator that maximize the probability of observe certains values of the variable ( endogenous variable). That is the reason why we must multiply the | Likelihood - Why multiply?
The goal of the maximum likelihood method is find estimator that maximize the probability of observe certains values of the variable ( endogenous variable). That is the reason why we must multiply the probabilties of ocurrence.
For example: imagine that the numbers of phone calls that a secr... | Likelihood - Why multiply?
The goal of the maximum likelihood method is find estimator that maximize the probability of observe certains values of the variable ( endogenous variable). That is the reason why we must multiply the |
7,594 | Likelihood - Why multiply? | In practice, when we want to find MLE we would usually take the log of the likelihood function. Then the product of density function or probability (depend on the data type) shall become sum.
As to the question why likelihood is a product of probability. Considering "The goal of maximum likelihood estimation is to fin... | Likelihood - Why multiply? | In practice, when we want to find MLE we would usually take the log of the likelihood function. Then the product of density function or probability (depend on the data type) shall become sum.
As to th | Likelihood - Why multiply?
In practice, when we want to find MLE we would usually take the log of the likelihood function. Then the product of density function or probability (depend on the data type) shall become sum.
As to the question why likelihood is a product of probability. Considering "The goal of maximum like... | Likelihood - Why multiply?
In practice, when we want to find MLE we would usually take the log of the likelihood function. Then the product of density function or probability (depend on the data type) shall become sum.
As to th |
7,595 | What is the difference between logistic regression and neural networks? | I assume you're thinking of what used to be, and perhaps still are referred to as 'multilayer perceptrons' in your question about neural networks. If so then I'd explain the whole thing in terms of flexibility about the form of the decision boundary as a function of explanatory variables. In particular, for this audi... | What is the difference between logistic regression and neural networks? | I assume you're thinking of what used to be, and perhaps still are referred to as 'multilayer perceptrons' in your question about neural networks. If so then I'd explain the whole thing in terms of f | What is the difference between logistic regression and neural networks?
I assume you're thinking of what used to be, and perhaps still are referred to as 'multilayer perceptrons' in your question about neural networks. If so then I'd explain the whole thing in terms of flexibility about the form of the decision bounda... | What is the difference between logistic regression and neural networks?
I assume you're thinking of what used to be, and perhaps still are referred to as 'multilayer perceptrons' in your question about neural networks. If so then I'd explain the whole thing in terms of f |
7,596 | What is the difference between logistic regression and neural networks? | For a simpler summary:
Logistic regression: The simplest form of Neural Network, that results in decision boundaries that are a straight line
Neural Networks: A superset that includes Logistic regression and also other classifiers that can generate more complex decision boundaries.
(note: I'm referring to "plain" log... | What is the difference between logistic regression and neural networks? | For a simpler summary:
Logistic regression: The simplest form of Neural Network, that results in decision boundaries that are a straight line
Neural Networks: A superset that includes Logistic regres | What is the difference between logistic regression and neural networks?
For a simpler summary:
Logistic regression: The simplest form of Neural Network, that results in decision boundaries that are a straight line
Neural Networks: A superset that includes Logistic regression and also other classifiers that can generat... | What is the difference between logistic regression and neural networks?
For a simpler summary:
Logistic regression: The simplest form of Neural Network, that results in decision boundaries that are a straight line
Neural Networks: A superset that includes Logistic regres |
7,597 | What is the difference between logistic regression and neural networks? | I am going to take the question literally: Someone with no background in statistics. And I'm not going to try to give that person a background in statistics. For instance, suppose you have to explain the difference to the CEO of a company or something like that.
So: Logistic regression is a tool for modeling a categ... | What is the difference between logistic regression and neural networks? | I am going to take the question literally: Someone with no background in statistics. And I'm not going to try to give that person a background in statistics. For instance, suppose you have to explain | What is the difference between logistic regression and neural networks?
I am going to take the question literally: Someone with no background in statistics. And I'm not going to try to give that person a background in statistics. For instance, suppose you have to explain the difference to the CEO of a company or somet... | What is the difference between logistic regression and neural networks?
I am going to take the question literally: Someone with no background in statistics. And I'm not going to try to give that person a background in statistics. For instance, suppose you have to explain |
7,598 | What is the difference between logistic regression and neural networks? | I was taught that you can think of neural networks (with logistic activation functions) as as a weighted average of logit functions, with the weights themselves estimated. By choosing a large number of logits, you can fit any functional form. There's some graphical intuition in the Econometric Sense blog post. | What is the difference between logistic regression and neural networks? | I was taught that you can think of neural networks (with logistic activation functions) as as a weighted average of logit functions, with the weights themselves estimated. By choosing a large number o | What is the difference between logistic regression and neural networks?
I was taught that you can think of neural networks (with logistic activation functions) as as a weighted average of logit functions, with the weights themselves estimated. By choosing a large number of logits, you can fit any functional form. There... | What is the difference between logistic regression and neural networks?
I was taught that you can think of neural networks (with logistic activation functions) as as a weighted average of logit functions, with the weights themselves estimated. By choosing a large number o |
7,599 | What is the difference between logistic regression and neural networks? | The other answers are great. I would simply add some pictures showing that you can think of logistic regression and multi-class logistic regression (a.k.a. maxent, multinomial logistic regression, softmax regression, maximum entropy classifier) as a special architecture of neural networks.
From Sebastian Raschka, Michi... | What is the difference between logistic regression and neural networks? | The other answers are great. I would simply add some pictures showing that you can think of logistic regression and multi-class logistic regression (a.k.a. maxent, multinomial logistic regression, sof | What is the difference between logistic regression and neural networks?
The other answers are great. I would simply add some pictures showing that you can think of logistic regression and multi-class logistic regression (a.k.a. maxent, multinomial logistic regression, softmax regression, maximum entropy classifier) as ... | What is the difference between logistic regression and neural networks?
The other answers are great. I would simply add some pictures showing that you can think of logistic regression and multi-class logistic regression (a.k.a. maxent, multinomial logistic regression, sof |
7,600 | What is the difference between logistic regression and neural networks? | I would use an example of a complicated but concrete problem the audience understands. Use hidden nodes whose interpretations are not trained, but have particular meanings.
If you use chess positions (predicting whether white will win), you could let the inputs be a representation of the board (ignore whether you can c... | What is the difference between logistic regression and neural networks? | I would use an example of a complicated but concrete problem the audience understands. Use hidden nodes whose interpretations are not trained, but have particular meanings.
If you use chess positions | What is the difference between logistic regression and neural networks?
I would use an example of a complicated but concrete problem the audience understands. Use hidden nodes whose interpretations are not trained, but have particular meanings.
If you use chess positions (predicting whether white will win), you could l... | What is the difference between logistic regression and neural networks?
I would use an example of a complicated but concrete problem the audience understands. Use hidden nodes whose interpretations are not trained, but have particular meanings.
If you use chess positions |
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