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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1,601 | Validation Error less than training error? | I don't have enough points to comment on @D-K's answer, but this is now answered as a FAQ on Keras' documentation:
Why is my training loss much higher than my testing loss?
A Keras model has two modes: training and testing. Regularization mechanisms, such as Dropout and L1/L2 weight regularization, are turned off at t... | Validation Error less than training error? | I don't have enough points to comment on @D-K's answer, but this is now answered as a FAQ on Keras' documentation:
Why is my training loss much higher than my testing loss?
A Keras model has two mode | Validation Error less than training error?
I don't have enough points to comment on @D-K's answer, but this is now answered as a FAQ on Keras' documentation:
Why is my training loss much higher than my testing loss?
A Keras model has two modes: training and testing. Regularization mechanisms, such as Dropout and L1/L2... | Validation Error less than training error?
I don't have enough points to comment on @D-K's answer, but this is now answered as a FAQ on Keras' documentation:
Why is my training loss much higher than my testing loss?
A Keras model has two mode |
1,602 | Validation Error less than training error? | my 2 cents:
I also had the same problem even without having dropout layers.
In my case - batch-norm layers were the culprits.
When I deleted them - training loss became similar to validation loss.
Probably, that happened because during training batch-norm
uses mean and variance of the given input batch, which might be... | Validation Error less than training error? | my 2 cents:
I also had the same problem even without having dropout layers.
In my case - batch-norm layers were the culprits.
When I deleted them - training loss became similar to validation loss.
Pr | Validation Error less than training error?
my 2 cents:
I also had the same problem even without having dropout layers.
In my case - batch-norm layers were the culprits.
When I deleted them - training loss became similar to validation loss.
Probably, that happened because during training batch-norm
uses mean and varian... | Validation Error less than training error?
my 2 cents:
I also had the same problem even without having dropout layers.
In my case - batch-norm layers were the culprits.
When I deleted them - training loss became similar to validation loss.
Pr |
1,603 | Validation Error less than training error? | Another possibility that combines both the answer of @cdeterman and @D-K in some way is if you're using some data augmentation mechanism. Infact data augmentation is usually done only on training set and not on validation set (as for the dropout regularization), and this may lead to a validation set containing "easier"... | Validation Error less than training error? | Another possibility that combines both the answer of @cdeterman and @D-K in some way is if you're using some data augmentation mechanism. Infact data augmentation is usually done only on training set | Validation Error less than training error?
Another possibility that combines both the answer of @cdeterman and @D-K in some way is if you're using some data augmentation mechanism. Infact data augmentation is usually done only on training set and not on validation set (as for the dropout regularization), and this may l... | Validation Error less than training error?
Another possibility that combines both the answer of @cdeterman and @D-K in some way is if you're using some data augmentation mechanism. Infact data augmentation is usually done only on training set |
1,604 | Validation Error less than training error? | At this time, stochastic gradient based methods are almost always the algorithm of choice for deep learning. This means that data comes in as batches, gradients are computed and parameters are updated. This means you can also compute the loss over the data as each batch is selected. Under this framework, there are two ... | Validation Error less than training error? | At this time, stochastic gradient based methods are almost always the algorithm of choice for deep learning. This means that data comes in as batches, gradients are computed and parameters are updated | Validation Error less than training error?
At this time, stochastic gradient based methods are almost always the algorithm of choice for deep learning. This means that data comes in as batches, gradients are computed and parameters are updated. This means you can also compute the loss over the data as each batch is sel... | Validation Error less than training error?
At this time, stochastic gradient based methods are almost always the algorithm of choice for deep learning. This means that data comes in as batches, gradients are computed and parameters are updated |
1,605 | Validation Error less than training error? | I got similar results (test loss was significantly lower than training loss). Once I removed the dropout regularization, both the loss became almost equal. | Validation Error less than training error? | I got similar results (test loss was significantly lower than training loss). Once I removed the dropout regularization, both the loss became almost equal. | Validation Error less than training error?
I got similar results (test loss was significantly lower than training loss). Once I removed the dropout regularization, both the loss became almost equal. | Validation Error less than training error?
I got similar results (test loss was significantly lower than training loss). Once I removed the dropout regularization, both the loss became almost equal. |
1,606 | Validation Error less than training error? | @cdeterman and @D-K have good explanation. I would like to one more reason - data leakage. Some part of your train-data are "closely related" with the test-data.
Potential example: imagine you have 1000 dogs and 1000 cats with 500 similar pictures per pet (some owners love to take pictures of their pets in very simila... | Validation Error less than training error? | @cdeterman and @D-K have good explanation. I would like to one more reason - data leakage. Some part of your train-data are "closely related" with the test-data.
Potential example: imagine you have 1 | Validation Error less than training error?
@cdeterman and @D-K have good explanation. I would like to one more reason - data leakage. Some part of your train-data are "closely related" with the test-data.
Potential example: imagine you have 1000 dogs and 1000 cats with 500 similar pictures per pet (some owners love to... | Validation Error less than training error?
@cdeterman and @D-K have good explanation. I would like to one more reason - data leakage. Some part of your train-data are "closely related" with the test-data.
Potential example: imagine you have 1 |
1,607 | Validation Error less than training error? | A lower validation than training error can be caused by fluctuations associated with dropout or else, but if it persists in the long run this may indicate that the training and validation datasets were not actually drawn from the same statistical ensembles. This could happen if your examples come from a series and if y... | Validation Error less than training error? | A lower validation than training error can be caused by fluctuations associated with dropout or else, but if it persists in the long run this may indicate that the training and validation datasets wer | Validation Error less than training error?
A lower validation than training error can be caused by fluctuations associated with dropout or else, but if it persists in the long run this may indicate that the training and validation datasets were not actually drawn from the same statistical ensembles. This could happen i... | Validation Error less than training error?
A lower validation than training error can be caused by fluctuations associated with dropout or else, but if it persists in the long run this may indicate that the training and validation datasets wer |
1,608 | Validation Error less than training error? | Simply put, if training loss and validation loss are computed correctly, it is impossible for training loss to be higher than validation loss. This is because back-propagation DIRECTLY reduces error computed on the training set and only INDIRECTLY (not even guaranteed!) reduces error computed on the validation set.
Th... | Validation Error less than training error? | Simply put, if training loss and validation loss are computed correctly, it is impossible for training loss to be higher than validation loss. This is because back-propagation DIRECTLY reduces error c | Validation Error less than training error?
Simply put, if training loss and validation loss are computed correctly, it is impossible for training loss to be higher than validation loss. This is because back-propagation DIRECTLY reduces error computed on the training set and only INDIRECTLY (not even guaranteed!) reduce... | Validation Error less than training error?
Simply put, if training loss and validation loss are computed correctly, it is impossible for training loss to be higher than validation loss. This is because back-propagation DIRECTLY reduces error c |
1,609 | "Best" series of colors to use for differentiating series in publication-quality plots | A common reference for choosing a color palette is the work of Cynthia Brewer on ColorBrewer. The colors were chosen based on perceptual patterns in choropleth maps, but most of the same advice applies to using color in any type of plot to distinguish data patterns. If color is solely to distinguish between the differe... | "Best" series of colors to use for differentiating series in publication-quality plots | A common reference for choosing a color palette is the work of Cynthia Brewer on ColorBrewer. The colors were chosen based on perceptual patterns in choropleth maps, but most of the same advice applie | "Best" series of colors to use for differentiating series in publication-quality plots
A common reference for choosing a color palette is the work of Cynthia Brewer on ColorBrewer. The colors were chosen based on perceptual patterns in choropleth maps, but most of the same advice applies to using color in any type of p... | "Best" series of colors to use for differentiating series in publication-quality plots
A common reference for choosing a color palette is the work of Cynthia Brewer on ColorBrewer. The colors were chosen based on perceptual patterns in choropleth maps, but most of the same advice applie |
1,610 | "Best" series of colors to use for differentiating series in publication-quality plots | Much outstandingly good advice in other answers, but here are some extra points from my own low-level advice to students. This is all just advice, naturally, to be thought about given the key questions: What is my graph intended to do? What makes sense with these data? Who are the readership? What I am expecting colour... | "Best" series of colors to use for differentiating series in publication-quality plots | Much outstandingly good advice in other answers, but here are some extra points from my own low-level advice to students. This is all just advice, naturally, to be thought about given the key question | "Best" series of colors to use for differentiating series in publication-quality plots
Much outstandingly good advice in other answers, but here are some extra points from my own low-level advice to students. This is all just advice, naturally, to be thought about given the key questions: What is my graph intended to d... | "Best" series of colors to use for differentiating series in publication-quality plots
Much outstandingly good advice in other answers, but here are some extra points from my own low-level advice to students. This is all just advice, naturally, to be thought about given the key question |
1,611 | "Best" series of colors to use for differentiating series in publication-quality plots | Paul Tol provides a colour scheme optimised for colour differences (i.e., categorical or qualitative data) and colour-blind vision on his website, and in detail in a "technote" (PDF file) linked to there. He states:
To make graphics with your scientific results as clear as possible, it
is handy to have a palette of co... | "Best" series of colors to use for differentiating series in publication-quality plots | Paul Tol provides a colour scheme optimised for colour differences (i.e., categorical or qualitative data) and colour-blind vision on his website, and in detail in a "technote" (PDF file) linked to th | "Best" series of colors to use for differentiating series in publication-quality plots
Paul Tol provides a colour scheme optimised for colour differences (i.e., categorical or qualitative data) and colour-blind vision on his website, and in detail in a "technote" (PDF file) linked to there. He states:
To make graphics... | "Best" series of colors to use for differentiating series in publication-quality plots
Paul Tol provides a colour scheme optimised for colour differences (i.e., categorical or qualitative data) and colour-blind vision on his website, and in detail in a "technote" (PDF file) linked to th |
1,612 | "Best" series of colors to use for differentiating series in publication-quality plots | There's actually been a good deal of research on this in recent years.
A big point is "semantic resonance." This basically means "colors that correspond to what they represent," e.g. a time series for money should be colored green, at least for an audience in the USA. This apparently improves comprehension. One very in... | "Best" series of colors to use for differentiating series in publication-quality plots | There's actually been a good deal of research on this in recent years.
A big point is "semantic resonance." This basically means "colors that correspond to what they represent," e.g. a time series for | "Best" series of colors to use for differentiating series in publication-quality plots
There's actually been a good deal of research on this in recent years.
A big point is "semantic resonance." This basically means "colors that correspond to what they represent," e.g. a time series for money should be colored green, a... | "Best" series of colors to use for differentiating series in publication-quality plots
There's actually been a good deal of research on this in recent years.
A big point is "semantic resonance." This basically means "colors that correspond to what they represent," e.g. a time series for |
1,613 | "Best" series of colors to use for differentiating series in publication-quality plots | On colorbrewer2.org you can find qualitative, sequential and diverging colour schemes. Qualitative maximizes the difference between successive colours, and that's what I am using in gnuplot. The beauty of the site is that you can easily copy the hexadecimal codes of the colours so they are a breeze to import. As an exa... | "Best" series of colors to use for differentiating series in publication-quality plots | On colorbrewer2.org you can find qualitative, sequential and diverging colour schemes. Qualitative maximizes the difference between successive colours, and that's what I am using in gnuplot. The beaut | "Best" series of colors to use for differentiating series in publication-quality plots
On colorbrewer2.org you can find qualitative, sequential and diverging colour schemes. Qualitative maximizes the difference between successive colours, and that's what I am using in gnuplot. The beauty of the site is that you can eas... | "Best" series of colors to use for differentiating series in publication-quality plots
On colorbrewer2.org you can find qualitative, sequential and diverging colour schemes. Qualitative maximizes the difference between successive colours, and that's what I am using in gnuplot. The beaut |
1,614 | "Best" series of colors to use for differentiating series in publication-quality plots | There are plenty of websites dedicated to choosing color palettes. I don't know that there is a particular set of colors that is objectively the best, you will have to choose based on your audience and the tone of your work.
Check out http://www.colourlovers.com/palettes or http://design-seeds.com/index.php/search to g... | "Best" series of colors to use for differentiating series in publication-quality plots | There are plenty of websites dedicated to choosing color palettes. I don't know that there is a particular set of colors that is objectively the best, you will have to choose based on your audience an | "Best" series of colors to use for differentiating series in publication-quality plots
There are plenty of websites dedicated to choosing color palettes. I don't know that there is a particular set of colors that is objectively the best, you will have to choose based on your audience and the tone of your work.
Check ou... | "Best" series of colors to use for differentiating series in publication-quality plots
There are plenty of websites dedicated to choosing color palettes. I don't know that there is a particular set of colors that is objectively the best, you will have to choose based on your audience an |
1,615 | "Best" series of colors to use for differentiating series in publication-quality plots | For colorblind viewers, CARTOColors has a qualitative colorblind-friendly scheme called Safe that is based on Paul Tol's popular colour schemes. This palette consists of 12 easily distinguishable colours.
Another great qualitative colorblind friendly palette is the Okabe and Ito scheme proposed in their article “Color... | "Best" series of colors to use for differentiating series in publication-quality plots | For colorblind viewers, CARTOColors has a qualitative colorblind-friendly scheme called Safe that is based on Paul Tol's popular colour schemes. This palette consists of 12 easily distinguishable colo | "Best" series of colors to use for differentiating series in publication-quality plots
For colorblind viewers, CARTOColors has a qualitative colorblind-friendly scheme called Safe that is based on Paul Tol's popular colour schemes. This palette consists of 12 easily distinguishable colours.
Another great qualitative c... | "Best" series of colors to use for differentiating series in publication-quality plots
For colorblind viewers, CARTOColors has a qualitative colorblind-friendly scheme called Safe that is based on Paul Tol's popular colour schemes. This palette consists of 12 easily distinguishable colo |
1,616 | "Best" series of colors to use for differentiating series in publication-quality plots | I like the Dark2 palette from colorbrewer for scatter plots. We used this in the ggobi book, www.ggobi.org/book. But otherwise the color palettes are meant for geographic areas rather than data plots. Good color choice is still an issue for point-based plots.
The R packages colorspace and dichromat are useful. colorsp... | "Best" series of colors to use for differentiating series in publication-quality plots | I like the Dark2 palette from colorbrewer for scatter plots. We used this in the ggobi book, www.ggobi.org/book. But otherwise the color palettes are meant for geographic areas rather than data plots. | "Best" series of colors to use for differentiating series in publication-quality plots
I like the Dark2 palette from colorbrewer for scatter plots. We used this in the ggobi book, www.ggobi.org/book. But otherwise the color palettes are meant for geographic areas rather than data plots. Good color choice is still an is... | "Best" series of colors to use for differentiating series in publication-quality plots
I like the Dark2 palette from colorbrewer for scatter plots. We used this in the ggobi book, www.ggobi.org/book. But otherwise the color palettes are meant for geographic areas rather than data plots. |
1,617 | "Best" series of colors to use for differentiating series in publication-quality plots | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
This is my favourite scheme. It has 20 (!!!!) distinct... | "Best" series of colors to use for differentiating series in publication-quality plots | 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.
| "Best" series of colors to use for differentiating series in publication-quality plots
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.
... | "Best" series of colors to use for differentiating series in publication-quality plots
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.
|
1,618 | "Best" series of colors to use for differentiating series in publication-quality plots | Another possibility would be to find a set of colors that are a) equidistant in LAB, b) take color blindness into consideration, and c) can fit into the gamut of the sRGB colorspace as well as the gamuts of the most common CMYK spaces.
I think the last requirement is a necessity for any method of picking colors- it doe... | "Best" series of colors to use for differentiating series in publication-quality plots | Another possibility would be to find a set of colors that are a) equidistant in LAB, b) take color blindness into consideration, and c) can fit into the gamut of the sRGB colorspace as well as the gam | "Best" series of colors to use for differentiating series in publication-quality plots
Another possibility would be to find a set of colors that are a) equidistant in LAB, b) take color blindness into consideration, and c) can fit into the gamut of the sRGB colorspace as well as the gamuts of the most common CMYK space... | "Best" series of colors to use for differentiating series in publication-quality plots
Another possibility would be to find a set of colors that are a) equidistant in LAB, b) take color blindness into consideration, and c) can fit into the gamut of the sRGB colorspace as well as the gam |
1,619 | "Best" series of colors to use for differentiating series in publication-quality plots | When plotting lines, you should watch out for green and yellow, which don't display well on projectors. Since I eventually re-use most of my plots in presentations, I avoid these colours even if the original intention is for screen or paper publication.
In the interests of maintaining high contrast, that leaves me with... | "Best" series of colors to use for differentiating series in publication-quality plots | When plotting lines, you should watch out for green and yellow, which don't display well on projectors. Since I eventually re-use most of my plots in presentations, I avoid these colours even if the o | "Best" series of colors to use for differentiating series in publication-quality plots
When plotting lines, you should watch out for green and yellow, which don't display well on projectors. Since I eventually re-use most of my plots in presentations, I avoid these colours even if the original intention is for screen o... | "Best" series of colors to use for differentiating series in publication-quality plots
When plotting lines, you should watch out for green and yellow, which don't display well on projectors. Since I eventually re-use most of my plots in presentations, I avoid these colours even if the o |
1,620 | What is the relation between k-means clustering and PCA? | It is true that K-means clustering and PCA appear to have very different goals and at first sight do not seem to be related. However, as explained in the Ding & He 2004 paper K-means Clustering via Principal Component Analysis, there is a deep connection between them.
The intuition is that PCA seeks to represent all $n... | What is the relation between k-means clustering and PCA? | It is true that K-means clustering and PCA appear to have very different goals and at first sight do not seem to be related. However, as explained in the Ding & He 2004 paper K-means Clustering via Pr | What is the relation between k-means clustering and PCA?
It is true that K-means clustering and PCA appear to have very different goals and at first sight do not seem to be related. However, as explained in the Ding & He 2004 paper K-means Clustering via Principal Component Analysis, there is a deep connection between ... | What is the relation between k-means clustering and PCA?
It is true that K-means clustering and PCA appear to have very different goals and at first sight do not seem to be related. However, as explained in the Ding & He 2004 paper K-means Clustering via Pr |
1,621 | What is the relation between k-means clustering and PCA? | PCA and K-means do different things.
PCA is used for dimensionality reduction / feature selection / representation learning e.g. when the feature space contains too many irrelevant or redundant features. The aim is to find the intrinsic dimensionality of the data.
Here's a two dimensional example that can be generalize... | What is the relation between k-means clustering and PCA? | PCA and K-means do different things.
PCA is used for dimensionality reduction / feature selection / representation learning e.g. when the feature space contains too many irrelevant or redundant featur | What is the relation between k-means clustering and PCA?
PCA and K-means do different things.
PCA is used for dimensionality reduction / feature selection / representation learning e.g. when the feature space contains too many irrelevant or redundant features. The aim is to find the intrinsic dimensionality of the data... | What is the relation between k-means clustering and PCA?
PCA and K-means do different things.
PCA is used for dimensionality reduction / feature selection / representation learning e.g. when the feature space contains too many irrelevant or redundant featur |
1,622 | What is the relation between k-means clustering and PCA? | It is common to whiten data before using k-means. The reason is that k-means is extremely sensitive to scale, and when you have mixed attributes there is no "true" scale anymore. Then you have to normalize, standardize, or whiten your data. None is perfect, but whitening will remove global correlation which can sometim... | What is the relation between k-means clustering and PCA? | It is common to whiten data before using k-means. The reason is that k-means is extremely sensitive to scale, and when you have mixed attributes there is no "true" scale anymore. Then you have to norm | What is the relation between k-means clustering and PCA?
It is common to whiten data before using k-means. The reason is that k-means is extremely sensitive to scale, and when you have mixed attributes there is no "true" scale anymore. Then you have to normalize, standardize, or whiten your data. None is perfect, but w... | What is the relation between k-means clustering and PCA?
It is common to whiten data before using k-means. The reason is that k-means is extremely sensitive to scale, and when you have mixed attributes there is no "true" scale anymore. Then you have to norm |
1,623 | What is the relation between k-means clustering and PCA? | Solving the k-means on its O(k/epsilon) low-rank approximation (i.e., projecting on the span of the first largest singular vectors as in PCA) would yield a (1+epsilon) approximation in term of multiplicative error.
Particularly, Projecting on the k-largest vector would yield 2-approximation.
In fact, the sum of squared... | What is the relation between k-means clustering and PCA? | Solving the k-means on its O(k/epsilon) low-rank approximation (i.e., projecting on the span of the first largest singular vectors as in PCA) would yield a (1+epsilon) approximation in term of multipl | What is the relation between k-means clustering and PCA?
Solving the k-means on its O(k/epsilon) low-rank approximation (i.e., projecting on the span of the first largest singular vectors as in PCA) would yield a (1+epsilon) approximation in term of multiplicative error.
Particularly, Projecting on the k-largest vector... | What is the relation between k-means clustering and PCA?
Solving the k-means on its O(k/epsilon) low-rank approximation (i.e., projecting on the span of the first largest singular vectors as in PCA) would yield a (1+epsilon) approximation in term of multipl |
1,624 | What is the relation between k-means clustering and PCA? | Intuitive relationship of PCA and KMeans
Theoretically PCA dimensional analysis (the first K dimension retaining say the 90% of variance...does not need to have direct relationship with K Means cluster), however the value of using PCA came from
a) practical consideration given the nature of objects that we analyse ten... | What is the relation between k-means clustering and PCA? | Intuitive relationship of PCA and KMeans
Theoretically PCA dimensional analysis (the first K dimension retaining say the 90% of variance...does not need to have direct relationship with K Means clust | What is the relation between k-means clustering and PCA?
Intuitive relationship of PCA and KMeans
Theoretically PCA dimensional analysis (the first K dimension retaining say the 90% of variance...does not need to have direct relationship with K Means cluster), however the value of using PCA came from
a) practical cons... | What is the relation between k-means clustering and PCA?
Intuitive relationship of PCA and KMeans
Theoretically PCA dimensional analysis (the first K dimension retaining say the 90% of variance...does not need to have direct relationship with K Means clust |
1,625 | What is the relation between k-means clustering and PCA? | In a recent paper, we found that PCA is able to compress the Euclidean distance of intra-cluster pairs while preserving Euclidean distance of inter-cluster pairs.
Notice that K-means aims to minimize Euclidean distance to the centers. Hence the compressibility of PCA helps a lot.
This phenomenon can also be theoretical... | What is the relation between k-means clustering and PCA? | In a recent paper, we found that PCA is able to compress the Euclidean distance of intra-cluster pairs while preserving Euclidean distance of inter-cluster pairs.
Notice that K-means aims to minimize | What is the relation between k-means clustering and PCA?
In a recent paper, we found that PCA is able to compress the Euclidean distance of intra-cluster pairs while preserving Euclidean distance of inter-cluster pairs.
Notice that K-means aims to minimize Euclidean distance to the centers. Hence the compressibility of... | What is the relation between k-means clustering and PCA?
In a recent paper, we found that PCA is able to compress the Euclidean distance of intra-cluster pairs while preserving Euclidean distance of inter-cluster pairs.
Notice that K-means aims to minimize |
1,626 | Mean absolute error OR root mean squared error? | This depends on your loss function. In many circumstances it makes sense to give more weight to points further away from the mean--that is, being off by 10 is more than twice as bad as being off by 5. In such cases RMSE is a more appropriate measure of error.
If being off by ten is just twice as bad as being off by 5, ... | Mean absolute error OR root mean squared error? | This depends on your loss function. In many circumstances it makes sense to give more weight to points further away from the mean--that is, being off by 10 is more than twice as bad as being off by 5. | Mean absolute error OR root mean squared error?
This depends on your loss function. In many circumstances it makes sense to give more weight to points further away from the mean--that is, being off by 10 is more than twice as bad as being off by 5. In such cases RMSE is a more appropriate measure of error.
If being off... | Mean absolute error OR root mean squared error?
This depends on your loss function. In many circumstances it makes sense to give more weight to points further away from the mean--that is, being off by 10 is more than twice as bad as being off by 5. |
1,627 | Mean absolute error OR root mean squared error? | Here is another situation when you want to use (R)MSE instead of MAE: when your observations' conditional distribution is asymmetric and you want an unbiased fit. The (R)MSE is minimized by the conditional mean, the MAE by the conditional median. So if you minimize the MAE, the fit will be closer to the median and bias... | Mean absolute error OR root mean squared error? | Here is another situation when you want to use (R)MSE instead of MAE: when your observations' conditional distribution is asymmetric and you want an unbiased fit. The (R)MSE is minimized by the condit | Mean absolute error OR root mean squared error?
Here is another situation when you want to use (R)MSE instead of MAE: when your observations' conditional distribution is asymmetric and you want an unbiased fit. The (R)MSE is minimized by the conditional mean, the MAE by the conditional median. So if you minimize the MA... | Mean absolute error OR root mean squared error?
Here is another situation when you want to use (R)MSE instead of MAE: when your observations' conditional distribution is asymmetric and you want an unbiased fit. The (R)MSE is minimized by the condit |
1,628 | Mean absolute error OR root mean squared error? | RMSE is a more natural way of describing loss in Euclidean distance. Therefore if you graph it out in 3D, the loss is in a cone shape, as you can see above in green. This also applies to higher dimensions, although it's harder to visualize it.
MAE can be thought of as city-block distance. It isn't really as natural of ... | Mean absolute error OR root mean squared error? | RMSE is a more natural way of describing loss in Euclidean distance. Therefore if you graph it out in 3D, the loss is in a cone shape, as you can see above in green. This also applies to higher dimens | Mean absolute error OR root mean squared error?
RMSE is a more natural way of describing loss in Euclidean distance. Therefore if you graph it out in 3D, the loss is in a cone shape, as you can see above in green. This also applies to higher dimensions, although it's harder to visualize it.
MAE can be thought of as cit... | Mean absolute error OR root mean squared error?
RMSE is a more natural way of describing loss in Euclidean distance. Therefore if you graph it out in 3D, the loss is in a cone shape, as you can see above in green. This also applies to higher dimens |
1,629 | Mean absolute error OR root mean squared error? | To put it in short, if there are many outliers then you may consider using Mean Absolute Error (also called the Average Absolute Deviation). RMSE is more sensitive to outliers than the MAE. But when outliers are exponentially rare (like in a bell-shaped curve), the RMSE performs very well and is generally preferred.
Bo... | Mean absolute error OR root mean squared error? | To put it in short, if there are many outliers then you may consider using Mean Absolute Error (also called the Average Absolute Deviation). RMSE is more sensitive to outliers than the MAE. But when o | Mean absolute error OR root mean squared error?
To put it in short, if there are many outliers then you may consider using Mean Absolute Error (also called the Average Absolute Deviation). RMSE is more sensitive to outliers than the MAE. But when outliers are exponentially rare (like in a bell-shaped curve), the RMSE p... | Mean absolute error OR root mean squared error?
To put it in short, if there are many outliers then you may consider using Mean Absolute Error (also called the Average Absolute Deviation). RMSE is more sensitive to outliers than the MAE. But when o |
1,630 | Mean absolute error OR root mean squared error? | When prediction is less focal than parameter estimation, the Gauss-Markov theorem might be relevant:
In a linear model with spherical errors, OLS - the solution to the MSE minimization problem - is efficient in a class of linear unbiased estimators - there are (restrictive, to be sure) conditions under which "you can't... | Mean absolute error OR root mean squared error? | When prediction is less focal than parameter estimation, the Gauss-Markov theorem might be relevant:
In a linear model with spherical errors, OLS - the solution to the MSE minimization problem - is ef | Mean absolute error OR root mean squared error?
When prediction is less focal than parameter estimation, the Gauss-Markov theorem might be relevant:
In a linear model with spherical errors, OLS - the solution to the MSE minimization problem - is efficient in a class of linear unbiased estimators - there are (restrictiv... | Mean absolute error OR root mean squared error?
When prediction is less focal than parameter estimation, the Gauss-Markov theorem might be relevant:
In a linear model with spherical errors, OLS - the solution to the MSE minimization problem - is ef |
1,631 | What is an embedding layer in a neural network? | Relation to Word2Vec
==========================================
Word2Vec in a simple picture:
(source: netdna-ssl.com)
More in-depth explanation:
I believe it's related to the recent Word2Vec innovation in natural language processing. Roughly, Word2Vec means our vocabulary is discrete and we will learn an map which wi... | What is an embedding layer in a neural network? | Relation to Word2Vec
==========================================
Word2Vec in a simple picture:
(source: netdna-ssl.com)
More in-depth explanation:
I believe it's related to the recent Word2Vec innovat | What is an embedding layer in a neural network?
Relation to Word2Vec
==========================================
Word2Vec in a simple picture:
(source: netdna-ssl.com)
More in-depth explanation:
I believe it's related to the recent Word2Vec innovation in natural language processing. Roughly, Word2Vec means our vocabula... | What is an embedding layer in a neural network?
Relation to Word2Vec
==========================================
Word2Vec in a simple picture:
(source: netdna-ssl.com)
More in-depth explanation:
I believe it's related to the recent Word2Vec innovat |
1,632 | What is an embedding layer in a neural network? | In https://stackoverflow.com/questions/45649520/explain-with-example-how-embedding-layers-in-keras-works/ I tried to prepare an example using 2 sentences, keras's texts_to_sequences
'This is a text' --> [0 0 1 2 3 4]
and embedding layer. Based on How does Keras 'Embedding' layer work? the embedding layer first initia... | What is an embedding layer in a neural network? | In https://stackoverflow.com/questions/45649520/explain-with-example-how-embedding-layers-in-keras-works/ I tried to prepare an example using 2 sentences, keras's texts_to_sequences
'This is a text' | What is an embedding layer in a neural network?
In https://stackoverflow.com/questions/45649520/explain-with-example-how-embedding-layers-in-keras-works/ I tried to prepare an example using 2 sentences, keras's texts_to_sequences
'This is a text' --> [0 0 1 2 3 4]
and embedding layer. Based on How does Keras 'Embeddi... | What is an embedding layer in a neural network?
In https://stackoverflow.com/questions/45649520/explain-with-example-how-embedding-layers-in-keras-works/ I tried to prepare an example using 2 sentences, keras's texts_to_sequences
'This is a text' |
1,633 | Difference between standard error and standard deviation | To complete the answer to the question, Ocram nicely addressed standard error but did not contrast it to standard deviation and did not mention the dependence on sample size. As a special case for the estimator consider the sample mean. The standard error for the mean is $\sigma \, / \, \sqrt{n}$ where $\sigma$ is th... | Difference between standard error and standard deviation | To complete the answer to the question, Ocram nicely addressed standard error but did not contrast it to standard deviation and did not mention the dependence on sample size. As a special case for th | Difference between standard error and standard deviation
To complete the answer to the question, Ocram nicely addressed standard error but did not contrast it to standard deviation and did not mention the dependence on sample size. As a special case for the estimator consider the sample mean. The standard error for t... | Difference between standard error and standard deviation
To complete the answer to the question, Ocram nicely addressed standard error but did not contrast it to standard deviation and did not mention the dependence on sample size. As a special case for th |
1,634 | Difference between standard error and standard deviation | Here is a more practical (and not mathematical) answer:
The SD (standard deviation) quantifies scatter — how much the values vary from one
another.
The SEM (standard error of the mean) quantifies how precisely you know the true mean of
the population. It takes into account both the value of the SD and
the sample siz... | Difference between standard error and standard deviation | Here is a more practical (and not mathematical) answer:
The SD (standard deviation) quantifies scatter — how much the values vary from one
another.
The SEM (standard error of the mean) quantifies h | Difference between standard error and standard deviation
Here is a more practical (and not mathematical) answer:
The SD (standard deviation) quantifies scatter — how much the values vary from one
another.
The SEM (standard error of the mean) quantifies how precisely you know the true mean of
the population. It takes... | Difference between standard error and standard deviation
Here is a more practical (and not mathematical) answer:
The SD (standard deviation) quantifies scatter — how much the values vary from one
another.
The SEM (standard error of the mean) quantifies h |
1,635 | Difference between standard error and standard deviation | Let $\theta$ be your parameter of interest for which you want to make inference. To do this, you have available to you a sample of observations $\mathbf{x} = \{x_1, \ldots, x_n \}$ along with some technique to obtain an estimate of $\theta$, $\hat{\theta}(\mathbf{x})$. In this notation, I have made explicit that $\hat{... | Difference between standard error and standard deviation | Let $\theta$ be your parameter of interest for which you want to make inference. To do this, you have available to you a sample of observations $\mathbf{x} = \{x_1, \ldots, x_n \}$ along with some tec | Difference between standard error and standard deviation
Let $\theta$ be your parameter of interest for which you want to make inference. To do this, you have available to you a sample of observations $\mathbf{x} = \{x_1, \ldots, x_n \}$ along with some technique to obtain an estimate of $\theta$, $\hat{\theta}(\mathbf... | Difference between standard error and standard deviation
Let $\theta$ be your parameter of interest for which you want to make inference. To do this, you have available to you a sample of observations $\mathbf{x} = \{x_1, \ldots, x_n \}$ along with some tec |
1,636 | Difference between standard error and standard deviation | (note that I'm focusing on standard error of the mean, which I believe the questioner was as well, but you can generate a standard error for any sample statistic)
The standard error is related to the standard deviation but they are not the same thing and increasing sample size does not make them closer together. Rather... | Difference between standard error and standard deviation | (note that I'm focusing on standard error of the mean, which I believe the questioner was as well, but you can generate a standard error for any sample statistic)
The standard error is related to the | Difference between standard error and standard deviation
(note that I'm focusing on standard error of the mean, which I believe the questioner was as well, but you can generate a standard error for any sample statistic)
The standard error is related to the standard deviation but they are not the same thing and increasi... | Difference between standard error and standard deviation
(note that I'm focusing on standard error of the mean, which I believe the questioner was as well, but you can generate a standard error for any sample statistic)
The standard error is related to the |
1,637 | Why do we need to normalize data before principal component analysis (PCA)? [duplicate] | Normalization is important in PCA since it is a variance maximizing exercise. It projects your original data onto directions which maximize the variance. The first plot below shows the amount of total variance explained in the different principal components wher we have not normalized the data. As you can see, it seems... | Why do we need to normalize data before principal component analysis (PCA)? [duplicate] | Normalization is important in PCA since it is a variance maximizing exercise. It projects your original data onto directions which maximize the variance. The first plot below shows the amount of total | Why do we need to normalize data before principal component analysis (PCA)? [duplicate]
Normalization is important in PCA since it is a variance maximizing exercise. It projects your original data onto directions which maximize the variance. The first plot below shows the amount of total variance explained in the diffe... | Why do we need to normalize data before principal component analysis (PCA)? [duplicate]
Normalization is important in PCA since it is a variance maximizing exercise. It projects your original data onto directions which maximize the variance. The first plot below shows the amount of total |
1,638 | Why do we need to normalize data before principal component analysis (PCA)? [duplicate] | The term normalization is used in many contexts, with distinct, but related, meanings. Basically, normalizing means transforming so as to render normal. When data are seen as vectors, normalizing means transforming the vector so that it has unit norm. When data are though of as random variables, normalizing means trans... | Why do we need to normalize data before principal component analysis (PCA)? [duplicate] | The term normalization is used in many contexts, with distinct, but related, meanings. Basically, normalizing means transforming so as to render normal. When data are seen as vectors, normalizing mean | Why do we need to normalize data before principal component analysis (PCA)? [duplicate]
The term normalization is used in many contexts, with distinct, but related, meanings. Basically, normalizing means transforming so as to render normal. When data are seen as vectors, normalizing means transforming the vector so tha... | Why do we need to normalize data before principal component analysis (PCA)? [duplicate]
The term normalization is used in many contexts, with distinct, but related, meanings. Basically, normalizing means transforming so as to render normal. When data are seen as vectors, normalizing mean |
1,639 | Detecting a given face in a database of facial images | A better idea might be to trash all images that appear in the feed of more than one user - no recognition needed. | Detecting a given face in a database of facial images | A better idea might be to trash all images that appear in the feed of more than one user - no recognition needed. | Detecting a given face in a database of facial images
A better idea might be to trash all images that appear in the feed of more than one user - no recognition needed. | Detecting a given face in a database of facial images
A better idea might be to trash all images that appear in the feed of more than one user - no recognition needed. |
1,640 | Detecting a given face in a database of facial images | I have a feeling that http://www.tineye.com/commercial_api may be the solution here.
Simply throw the Twitter profile image to Tineye, see if it returns images (and associated URLs) that can clearly be identified (or automatically scored using simple word-count logic) as being related to (or of) that little sack of **.... | Detecting a given face in a database of facial images | I have a feeling that http://www.tineye.com/commercial_api may be the solution here.
Simply throw the Twitter profile image to Tineye, see if it returns images (and associated URLs) that can clearly b | Detecting a given face in a database of facial images
I have a feeling that http://www.tineye.com/commercial_api may be the solution here.
Simply throw the Twitter profile image to Tineye, see if it returns images (and associated URLs) that can clearly be identified (or automatically scored using simple word-count logi... | Detecting a given face in a database of facial images
I have a feeling that http://www.tineye.com/commercial_api may be the solution here.
Simply throw the Twitter profile image to Tineye, see if it returns images (and associated URLs) that can clearly b |
1,641 | Detecting a given face in a database of facial images | Since you are able to filter to only those that are clear portrait photos, I'm assuming you have some method of feature generation to transform the raw images into features that are useful for machine learning purposes. If that's true, you could try to train a classification algorithm (there are lots of them: neural n... | Detecting a given face in a database of facial images | Since you are able to filter to only those that are clear portrait photos, I'm assuming you have some method of feature generation to transform the raw images into features that are useful for machine | Detecting a given face in a database of facial images
Since you are able to filter to only those that are clear portrait photos, I'm assuming you have some method of feature generation to transform the raw images into features that are useful for machine learning purposes. If that's true, you could try to train a clas... | Detecting a given face in a database of facial images
Since you are able to filter to only those that are clear portrait photos, I'm assuming you have some method of feature generation to transform the raw images into features that are useful for machine |
1,642 | Detecting a given face in a database of facial images | You could use a method like eigenfaces, http://en.wikipedia.org/wiki/Eigenface.
The following has a good walk through of the procedure as well as links to different implementations.
http://www.pages.drexel.edu/~sis26/Eigenface%20Tutorial.htm
From here it is common to use this in a classification approach, train a mod... | Detecting a given face in a database of facial images | You could use a method like eigenfaces, http://en.wikipedia.org/wiki/Eigenface.
The following has a good walk through of the procedure as well as links to different implementations.
http://www.pages. | Detecting a given face in a database of facial images
You could use a method like eigenfaces, http://en.wikipedia.org/wiki/Eigenface.
The following has a good walk through of the procedure as well as links to different implementations.
http://www.pages.drexel.edu/~sis26/Eigenface%20Tutorial.htm
From here it is common... | Detecting a given face in a database of facial images
You could use a method like eigenfaces, http://en.wikipedia.org/wiki/Eigenface.
The following has a good walk through of the procedure as well as links to different implementations.
http://www.pages. |
1,643 | Detecting a given face in a database of facial images | If you want to do it yourself, I would recommend using Intel's free and open source OpenCV (CV for computer vision) project.
http://opencv.willowgarage.com/
http://oreilly.com/catalog/9780596516130 | Detecting a given face in a database of facial images | If you want to do it yourself, I would recommend using Intel's free and open source OpenCV (CV for computer vision) project.
http://opencv.willowgarage.com/
http://oreilly.com/catalog/9780596516130 | Detecting a given face in a database of facial images
If you want to do it yourself, I would recommend using Intel's free and open source OpenCV (CV for computer vision) project.
http://opencv.willowgarage.com/
http://oreilly.com/catalog/9780596516130 | Detecting a given face in a database of facial images
If you want to do it yourself, I would recommend using Intel's free and open source OpenCV (CV for computer vision) project.
http://opencv.willowgarage.com/
http://oreilly.com/catalog/9780596516130 |
1,644 | Detecting a given face in a database of facial images | You need to put on an algorithm detecting which person that picture is referring to. You can build a model based on different portrait pictures of famous personality and use classifiers to ensure that this picture is referring to one of your database picture. You need to use a certain classifier based on different para... | Detecting a given face in a database of facial images | You need to put on an algorithm detecting which person that picture is referring to. You can build a model based on different portrait pictures of famous personality and use classifiers to ensure that | Detecting a given face in a database of facial images
You need to put on an algorithm detecting which person that picture is referring to. You can build a model based on different portrait pictures of famous personality and use classifiers to ensure that this picture is referring to one of your database picture. You ne... | Detecting a given face in a database of facial images
You need to put on an algorithm detecting which person that picture is referring to. You can build a model based on different portrait pictures of famous personality and use classifiers to ensure that |
1,645 | Detecting a given face in a database of facial images | You could try locality sensitive hashing. | Detecting a given face in a database of facial images | You could try locality sensitive hashing. | Detecting a given face in a database of facial images
You could try locality sensitive hashing. | Detecting a given face in a database of facial images
You could try locality sensitive hashing. |
1,646 | What is covariance in plain language? | Covariance is a measure of how changes in one variable are associated with changes in a second variable. Specifically, covariance measures the degree to which two variables are linearly associated. However, it is also often used informally as a general measure of how monotonically related two variables are. There are m... | What is covariance in plain language? | Covariance is a measure of how changes in one variable are associated with changes in a second variable. Specifically, covariance measures the degree to which two variables are linearly associated. Ho | What is covariance in plain language?
Covariance is a measure of how changes in one variable are associated with changes in a second variable. Specifically, covariance measures the degree to which two variables are linearly associated. However, it is also often used informally as a general measure of how monotonically ... | What is covariance in plain language?
Covariance is a measure of how changes in one variable are associated with changes in a second variable. Specifically, covariance measures the degree to which two variables are linearly associated. Ho |
1,647 | What is covariance in plain language? | Macro's answer is excellent, but I want to add more to a point of how covariance is related to correlation. Covariance doesn't really tell you about the strength of the relationship between the two variables, while correlation does. For example:
x = [1, 2, 3]
y = [4, 6, 10]
cov(x,y) = 2 #I am using population covarian... | What is covariance in plain language? | Macro's answer is excellent, but I want to add more to a point of how covariance is related to correlation. Covariance doesn't really tell you about the strength of the relationship between the two va | What is covariance in plain language?
Macro's answer is excellent, but I want to add more to a point of how covariance is related to correlation. Covariance doesn't really tell you about the strength of the relationship between the two variables, while correlation does. For example:
x = [1, 2, 3]
y = [4, 6, 10]
cov(x,... | What is covariance in plain language?
Macro's answer is excellent, but I want to add more to a point of how covariance is related to correlation. Covariance doesn't really tell you about the strength of the relationship between the two va |
1,648 | What is the difference between Multiclass and Multilabel Problem | I suspect the difference is that in multi-class problems the classes are mutually exclusive, whereas for multi-label problems each label represents a different classification task, but the tasks are somehow related (so there is a benefit in tackling them together rather than separately). For example, in the famous lep... | What is the difference between Multiclass and Multilabel Problem | I suspect the difference is that in multi-class problems the classes are mutually exclusive, whereas for multi-label problems each label represents a different classification task, but the tasks are s | What is the difference between Multiclass and Multilabel Problem
I suspect the difference is that in multi-class problems the classes are mutually exclusive, whereas for multi-label problems each label represents a different classification task, but the tasks are somehow related (so there is a benefit in tackling them ... | What is the difference between Multiclass and Multilabel Problem
I suspect the difference is that in multi-class problems the classes are mutually exclusive, whereas for multi-label problems each label represents a different classification task, but the tasks are s |
1,649 | What is the difference between Multiclass and Multilabel Problem | Multiclass classification means a classification task with more than two classes; e.g., classify a set of images of fruits which may be oranges, apples, or pears. Multiclass classification makes the assumption that each sample is assigned to one and only one label: a fruit can be either an apple or a pear but not both ... | What is the difference between Multiclass and Multilabel Problem | Multiclass classification means a classification task with more than two classes; e.g., classify a set of images of fruits which may be oranges, apples, or pears. Multiclass classification makes the a | What is the difference between Multiclass and Multilabel Problem
Multiclass classification means a classification task with more than two classes; e.g., classify a set of images of fruits which may be oranges, apples, or pears. Multiclass classification makes the assumption that each sample is assigned to one and only ... | What is the difference between Multiclass and Multilabel Problem
Multiclass classification means a classification task with more than two classes; e.g., classify a set of images of fruits which may be oranges, apples, or pears. Multiclass classification makes the a |
1,650 | What is the difference between Multiclass and Multilabel Problem | To complement the other answers, here are some figures. One row = the expected output for one sample.
Multiclass
One column = one class (one-hot encoding)
Multilabel
One column = one class
You see that:
in the multilabel case, one sample might be assigned more than one class.
in the multiclass case, there are more... | What is the difference between Multiclass and Multilabel Problem | To complement the other answers, here are some figures. One row = the expected output for one sample.
Multiclass
One column = one class (one-hot encoding)
Multilabel
One column = one class
You see | What is the difference between Multiclass and Multilabel Problem
To complement the other answers, here are some figures. One row = the expected output for one sample.
Multiclass
One column = one class (one-hot encoding)
Multilabel
One column = one class
You see that:
in the multilabel case, one sample might be assi... | What is the difference between Multiclass and Multilabel Problem
To complement the other answers, here are some figures. One row = the expected output for one sample.
Multiclass
One column = one class (one-hot encoding)
Multilabel
One column = one class
You see |
1,651 | What is the difference between Multiclass and Multilabel Problem | A multi-class problem has the assignment of instances to one of a finite, mutually-exclusive collection of classes. As in the example already given of crabs (from @Dikran): male-blue, female-blue, male-orange, female-orange. Each of these is exclusive of the others and taken together they are comprehensive.
One form ... | What is the difference between Multiclass and Multilabel Problem | A multi-class problem has the assignment of instances to one of a finite, mutually-exclusive collection of classes. As in the example already given of crabs (from @Dikran): male-blue, female-blue, ma | What is the difference between Multiclass and Multilabel Problem
A multi-class problem has the assignment of instances to one of a finite, mutually-exclusive collection of classes. As in the example already given of crabs (from @Dikran): male-blue, female-blue, male-orange, female-orange. Each of these is exclusive o... | What is the difference between Multiclass and Multilabel Problem
A multi-class problem has the assignment of instances to one of a finite, mutually-exclusive collection of classes. As in the example already given of crabs (from @Dikran): male-blue, female-blue, ma |
1,652 | What is the difference between Multiclass and Multilabel Problem | And one more difference lies in that the multi-label problem requires the model to learn the correlation between the different classes, but in multiclass problems different classes are independent of each other. | What is the difference between Multiclass and Multilabel Problem | And one more difference lies in that the multi-label problem requires the model to learn the correlation between the different classes, but in multiclass problems different classes are independent of | What is the difference between Multiclass and Multilabel Problem
And one more difference lies in that the multi-label problem requires the model to learn the correlation between the different classes, but in multiclass problems different classes are independent of each other. | What is the difference between Multiclass and Multilabel Problem
And one more difference lies in that the multi-label problem requires the model to learn the correlation between the different classes, but in multiclass problems different classes are independent of |
1,653 | What is the difference between Multiclass and Multilabel Problem | Multi Class classification Problem
One right answer and Mutually exclusive outputs(eg iris, numbers)
Multi Label Classification
more than one right answer and appropriate output or Non exclusive eg(sugar test, eye test)
In multi class we user softmax
In multi label we use sigmoid | What is the difference between Multiclass and Multilabel Problem | Multi Class classification Problem
One right answer and Mutually exclusive outputs(eg iris, numbers)
Multi Label Classification
more than one right answer and appropriate output or Non exclusive eg(s | What is the difference between Multiclass and Multilabel Problem
Multi Class classification Problem
One right answer and Mutually exclusive outputs(eg iris, numbers)
Multi Label Classification
more than one right answer and appropriate output or Non exclusive eg(sugar test, eye test)
In multi class we user softmax
In ... | What is the difference between Multiclass and Multilabel Problem
Multi Class classification Problem
One right answer and Mutually exclusive outputs(eg iris, numbers)
Multi Label Classification
more than one right answer and appropriate output or Non exclusive eg(s |
1,654 | Diagnostic plots for count regression | Here is what I usually like doing (for illustration I use the overdispersed and not very easily modelled quine data of pupil's days absent from school from MASS):
Test and graph the original count data by plotting observed frequencies and fitted frequencies (see chapter 2 in Friendly) which is supported by the vcd... | Diagnostic plots for count regression | Here is what I usually like doing (for illustration I use the overdispersed and not very easily modelled quine data of pupil's days absent from school from MASS):
Test and graph the original coun | Diagnostic plots for count regression
Here is what I usually like doing (for illustration I use the overdispersed and not very easily modelled quine data of pupil's days absent from school from MASS):
Test and graph the original count data by plotting observed frequencies and fitted frequencies (see chapter 2 in F... | Diagnostic plots for count regression
Here is what I usually like doing (for illustration I use the overdispersed and not very easily modelled quine data of pupil's days absent from school from MASS):
Test and graph the original coun |
1,655 | Diagnostic plots for count regression | For the approach of using standard diagnostic plots but wanting to know what they should look like, I like the paper:
Buja, A., Cook, D. Hofmann, H., Lawrence, M. Lee, E.-K., Swayne,
D.F and Wickham, H. (2009) Statistical Inference for exploratory
data analysis and model diagnostics Phil. Trans. R. Soc. A 2009
367... | Diagnostic plots for count regression | For the approach of using standard diagnostic plots but wanting to know what they should look like, I like the paper:
Buja, A., Cook, D. Hofmann, H., Lawrence, M. Lee, E.-K., Swayne,
D.F and Wickha | Diagnostic plots for count regression
For the approach of using standard diagnostic plots but wanting to know what they should look like, I like the paper:
Buja, A., Cook, D. Hofmann, H., Lawrence, M. Lee, E.-K., Swayne,
D.F and Wickham, H. (2009) Statistical Inference for exploratory
data analysis and model diagno... | Diagnostic plots for count regression
For the approach of using standard diagnostic plots but wanting to know what they should look like, I like the paper:
Buja, A., Cook, D. Hofmann, H., Lawrence, M. Lee, E.-K., Swayne,
D.F and Wickha |
1,656 | Diagnostic plots for count regression | This is an old question, but I thought it would be useful to add that my DHARMa R package (available from CRAN, see here) now provides standardized residuals for GLMs and GLMMs, based on a simulation approach similar to what is suggested by @GregSnow.
From the package description:
The DHARMa package uses a simulati... | Diagnostic plots for count regression | This is an old question, but I thought it would be useful to add that my DHARMa R package (available from CRAN, see here) now provides standardized residuals for GLMs and GLMMs, based on a simulation | Diagnostic plots for count regression
This is an old question, but I thought it would be useful to add that my DHARMa R package (available from CRAN, see here) now provides standardized residuals for GLMs and GLMMs, based on a simulation approach similar to what is suggested by @GregSnow.
From the package descriptio... | Diagnostic plots for count regression
This is an old question, but I thought it would be useful to add that my DHARMa R package (available from CRAN, see here) now provides standardized residuals for GLMs and GLMMs, based on a simulation |
1,657 | Diagnostic plots for count regression | There is a function called glm.diag.plots in package boot, to generate diagnostic plots for GLMs. What it does:
Makes plot of jackknife deviance residuals against linear predictor,
normal scores plots of standardized deviance residuals, plot of
approximate Cook statistics against leverage/(1-leverage), and case
... | Diagnostic plots for count regression | There is a function called glm.diag.plots in package boot, to generate diagnostic plots for GLMs. What it does:
Makes plot of jackknife deviance residuals against linear predictor,
normal scores pl | Diagnostic plots for count regression
There is a function called glm.diag.plots in package boot, to generate diagnostic plots for GLMs. What it does:
Makes plot of jackknife deviance residuals against linear predictor,
normal scores plots of standardized deviance residuals, plot of
approximate Cook statistics agai... | Diagnostic plots for count regression
There is a function called glm.diag.plots in package boot, to generate diagnostic plots for GLMs. What it does:
Makes plot of jackknife deviance residuals against linear predictor,
normal scores pl |
1,658 | Diagnostic plots for count regression | I would definitely recommend the {performance} package. It has a check_model(mod1) function that shows the relevant diagnostic plots.
library(MASS)
data(quine)
mod1 <- glm(Days~Age+Sex, data=quine, family="poisson")
summary(mod1)
#>
#> Call:
#> glm(formula = Days ~ Age + Sex, family = "poisson", data = quine)
#>
... | Diagnostic plots for count regression | I would definitely recommend the {performance} package. It has a check_model(mod1) function that shows the relevant diagnostic plots.
library(MASS)
data(quine)
mod1 <- glm(Days~Age+Sex, data=quine | Diagnostic plots for count regression
I would definitely recommend the {performance} package. It has a check_model(mod1) function that shows the relevant diagnostic plots.
library(MASS)
data(quine)
mod1 <- glm(Days~Age+Sex, data=quine, family="poisson")
summary(mod1)
#>
#> Call:
#> glm(formula = Days ~ Age + Sex, ... | Diagnostic plots for count regression
I would definitely recommend the {performance} package. It has a check_model(mod1) function that shows the relevant diagnostic plots.
library(MASS)
data(quine)
mod1 <- glm(Days~Age+Sex, data=quine |
1,659 | How to select kernel for SVM? | The kernel is effectively a similarity measure, so choosing a kernel according to prior knowledge of invariances as suggested by Robin (+1) is a good idea.
In the absence of expert knowledge, the Radial Basis Function kernel makes a good default kernel (once you have established it is a problem requiring a non-linear m... | How to select kernel for SVM? | The kernel is effectively a similarity measure, so choosing a kernel according to prior knowledge of invariances as suggested by Robin (+1) is a good idea.
In the absence of expert knowledge, the Radi | How to select kernel for SVM?
The kernel is effectively a similarity measure, so choosing a kernel according to prior knowledge of invariances as suggested by Robin (+1) is a good idea.
In the absence of expert knowledge, the Radial Basis Function kernel makes a good default kernel (once you have established it is a pr... | How to select kernel for SVM?
The kernel is effectively a similarity measure, so choosing a kernel according to prior knowledge of invariances as suggested by Robin (+1) is a good idea.
In the absence of expert knowledge, the Radi |
1,660 | How to select kernel for SVM? | If you are not sure what would be best you can use automatic techniques of selection (e.g. cross validation, ... ). In this case you can even use a combination of classifiers (if your problem is classification) obtained with different kernel.
However, the "advantage" of working with a kernel is that you change the usu... | How to select kernel for SVM? | If you are not sure what would be best you can use automatic techniques of selection (e.g. cross validation, ... ). In this case you can even use a combination of classifiers (if your problem is class | How to select kernel for SVM?
If you are not sure what would be best you can use automatic techniques of selection (e.g. cross validation, ... ). In this case you can even use a combination of classifiers (if your problem is classification) obtained with different kernel.
However, the "advantage" of working with a ker... | How to select kernel for SVM?
If you are not sure what would be best you can use automatic techniques of selection (e.g. cross validation, ... ). In this case you can even use a combination of classifiers (if your problem is class |
1,661 | How to select kernel for SVM? | I always have the feeling that any hyper parameter selection for SVMs is done via cross validation in combination with grid search. | How to select kernel for SVM? | I always have the feeling that any hyper parameter selection for SVMs is done via cross validation in combination with grid search. | How to select kernel for SVM?
I always have the feeling that any hyper parameter selection for SVMs is done via cross validation in combination with grid search. | How to select kernel for SVM?
I always have the feeling that any hyper parameter selection for SVMs is done via cross validation in combination with grid search. |
1,662 | How to select kernel for SVM? | In general, the RBF kernel is a reasonable rst choice.Furthermore,the linear kernel is a special case of RBF,In particular,when the number of features is very large, one may just use the linear kernel. | How to select kernel for SVM? | In general, the RBF kernel is a reasonable rst choice.Furthermore,the linear kernel is a special case of RBF,In particular,when the number of features is very large, one may just use the linear kernel | How to select kernel for SVM?
In general, the RBF kernel is a reasonable rst choice.Furthermore,the linear kernel is a special case of RBF,In particular,when the number of features is very large, one may just use the linear kernel. | How to select kernel for SVM?
In general, the RBF kernel is a reasonable rst choice.Furthermore,the linear kernel is a special case of RBF,In particular,when the number of features is very large, one may just use the linear kernel |
1,663 | Loadings vs eigenvectors in PCA: when to use one or another? | In PCA, you split covariance (or correlation) matrix into scale part (eigenvalues) and direction part (eigenvectors). You may then endow eigenvectors with the scale: loadings. So, loadings are thus become comparable by magnitude with the covariances/correlations observed between the variables, - because what had been d... | Loadings vs eigenvectors in PCA: when to use one or another? | In PCA, you split covariance (or correlation) matrix into scale part (eigenvalues) and direction part (eigenvectors). You may then endow eigenvectors with the scale: loadings. So, loadings are thus be | Loadings vs eigenvectors in PCA: when to use one or another?
In PCA, you split covariance (or correlation) matrix into scale part (eigenvalues) and direction part (eigenvectors). You may then endow eigenvectors with the scale: loadings. So, loadings are thus become comparable by magnitude with the covariances/correlati... | Loadings vs eigenvectors in PCA: when to use one or another?
In PCA, you split covariance (or correlation) matrix into scale part (eigenvalues) and direction part (eigenvectors). You may then endow eigenvectors with the scale: loadings. So, loadings are thus be |
1,664 | Loadings vs eigenvectors in PCA: when to use one or another? | There seems to be a great deal of confusion about loadings, coefficients and eigenvectors. The word loadings comes from Factor Analysis and it refers to coefficients of the regression of the data matrix onto the factors. They are not the coefficients defining the factors. See for example Mardia, Bibby and Kent or other... | Loadings vs eigenvectors in PCA: when to use one or another? | There seems to be a great deal of confusion about loadings, coefficients and eigenvectors. The word loadings comes from Factor Analysis and it refers to coefficients of the regression of the data matr | Loadings vs eigenvectors in PCA: when to use one or another?
There seems to be a great deal of confusion about loadings, coefficients and eigenvectors. The word loadings comes from Factor Analysis and it refers to coefficients of the regression of the data matrix onto the factors. They are not the coefficients defining... | Loadings vs eigenvectors in PCA: when to use one or another?
There seems to be a great deal of confusion about loadings, coefficients and eigenvectors. The word loadings comes from Factor Analysis and it refers to coefficients of the regression of the data matr |
1,665 | Loadings vs eigenvectors in PCA: when to use one or another? | I am a bit confused by those names, and I searched in the book named "Statistical Methods in the Atmospherical Science", and it gave me a summary of varied Terminology of PCA, here are the screenshots in the book, hope it will help. | Loadings vs eigenvectors in PCA: when to use one or another? | I am a bit confused by those names, and I searched in the book named "Statistical Methods in the Atmospherical Science", and it gave me a summary of varied Terminology of PCA, here are the screenshots | Loadings vs eigenvectors in PCA: when to use one or another?
I am a bit confused by those names, and I searched in the book named "Statistical Methods in the Atmospherical Science", and it gave me a summary of varied Terminology of PCA, here are the screenshots in the book, hope it will help. | Loadings vs eigenvectors in PCA: when to use one or another?
I am a bit confused by those names, and I searched in the book named "Statistical Methods in the Atmospherical Science", and it gave me a summary of varied Terminology of PCA, here are the screenshots |
1,666 | Loadings vs eigenvectors in PCA: when to use one or another? | There appears to be some confusion over this matter, so I will provide some observations and a pointer to where an excellent answer can be found in the literature.
Firstly, PCA and Factor Analysis (FA) are related. In general, principal components are orthogonal by definition whereas factors - the analogous entity in F... | Loadings vs eigenvectors in PCA: when to use one or another? | There appears to be some confusion over this matter, so I will provide some observations and a pointer to where an excellent answer can be found in the literature.
Firstly, PCA and Factor Analysis (FA | Loadings vs eigenvectors in PCA: when to use one or another?
There appears to be some confusion over this matter, so I will provide some observations and a pointer to where an excellent answer can be found in the literature.
Firstly, PCA and Factor Analysis (FA) are related. In general, principal components are orthogo... | Loadings vs eigenvectors in PCA: when to use one or another?
There appears to be some confusion over this matter, so I will provide some observations and a pointer to where an excellent answer can be found in the literature.
Firstly, PCA and Factor Analysis (FA |
1,667 | Loadings vs eigenvectors in PCA: when to use one or another? | 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.
The loadings show the relationship between the raw dat... | Loadings vs eigenvectors in PCA: when to use one or another? | 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.
| Loadings vs eigenvectors in PCA: when to use one or another?
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.
... | Loadings vs eigenvectors in PCA: when to use one or another?
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.
|
1,668 | Does the variance of a sum equal the sum of the variances? | The answer to your question is "Sometimes, but not in general".
To see this let $X_1, ..., X_n$ be random variables (with finite variances). Then,
$$ {\rm var} \left( \sum_{i=1}^{n} X_i \right) = E \left( \left[ \sum_{i=1}^{n} X_i \right]^2 \right) - \left[ E\left( \sum_{i=1}^{n} X_i \right) \right]^2$$
Now note that... | Does the variance of a sum equal the sum of the variances? | The answer to your question is "Sometimes, but not in general".
To see this let $X_1, ..., X_n$ be random variables (with finite variances). Then,
$$ {\rm var} \left( \sum_{i=1}^{n} X_i \right) = E | Does the variance of a sum equal the sum of the variances?
The answer to your question is "Sometimes, but not in general".
To see this let $X_1, ..., X_n$ be random variables (with finite variances). Then,
$$ {\rm var} \left( \sum_{i=1}^{n} X_i \right) = E \left( \left[ \sum_{i=1}^{n} X_i \right]^2 \right) - \left[ E... | Does the variance of a sum equal the sum of the variances?
The answer to your question is "Sometimes, but not in general".
To see this let $X_1, ..., X_n$ be random variables (with finite variances). Then,
$$ {\rm var} \left( \sum_{i=1}^{n} X_i \right) = E |
1,669 | Does the variance of a sum equal the sum of the variances? | $$\text{Var}\bigg(\sum_{i=1}^m X_i\bigg) = \sum_{i=1}^m \text{Var}(X_i) + 2\sum_{i\lt j} \text{Cov}(X_i,X_j).$$
So, if the covariances average to $0$, which would be a consequence if the variables are pairwise uncorrelated or if they are independent, then the variance of the sum is the sum of the variances.
An example... | Does the variance of a sum equal the sum of the variances? | $$\text{Var}\bigg(\sum_{i=1}^m X_i\bigg) = \sum_{i=1}^m \text{Var}(X_i) + 2\sum_{i\lt j} \text{Cov}(X_i,X_j).$$
So, if the covariances average to $0$, which would be a consequence if the variables are | Does the variance of a sum equal the sum of the variances?
$$\text{Var}\bigg(\sum_{i=1}^m X_i\bigg) = \sum_{i=1}^m \text{Var}(X_i) + 2\sum_{i\lt j} \text{Cov}(X_i,X_j).$$
So, if the covariances average to $0$, which would be a consequence if the variables are pairwise uncorrelated or if they are independent, then the v... | Does the variance of a sum equal the sum of the variances?
$$\text{Var}\bigg(\sum_{i=1}^m X_i\bigg) = \sum_{i=1}^m \text{Var}(X_i) + 2\sum_{i\lt j} \text{Cov}(X_i,X_j).$$
So, if the covariances average to $0$, which would be a consequence if the variables are |
1,670 | Does the variance of a sum equal the sum of the variances? | I just wanted to add a more succinct version of the proof given by Macro, so it's easier to see what's going on. $\newcommand{\Cov}{\text{Cov}}\newcommand{\Var}{\text{Var}}$
Notice that since $\Var(X) = \Cov(X,X)$
For any two random variables $X,Y$ we have:
\begin{align}
\Var(X+Y) &= \Cov(X+Y,X+Y) \\
&= E((X+Y)^2)-E... | Does the variance of a sum equal the sum of the variances? | I just wanted to add a more succinct version of the proof given by Macro, so it's easier to see what's going on. $\newcommand{\Cov}{\text{Cov}}\newcommand{\Var}{\text{Var}}$
Notice that since $\Var(X) | Does the variance of a sum equal the sum of the variances?
I just wanted to add a more succinct version of the proof given by Macro, so it's easier to see what's going on. $\newcommand{\Cov}{\text{Cov}}\newcommand{\Var}{\text{Var}}$
Notice that since $\Var(X) = \Cov(X,X)$
For any two random variables $X,Y$ we have:
\b... | Does the variance of a sum equal the sum of the variances?
I just wanted to add a more succinct version of the proof given by Macro, so it's easier to see what's going on. $\newcommand{\Cov}{\text{Cov}}\newcommand{\Var}{\text{Var}}$
Notice that since $\Var(X) |
1,671 | Does the variance of a sum equal the sum of the variances? | Yes, if each pair of the $X_i$'s are uncorrelated, this is true.
See the explanation on Wikipedia | Does the variance of a sum equal the sum of the variances? | Yes, if each pair of the $X_i$'s are uncorrelated, this is true.
See the explanation on Wikipedia | Does the variance of a sum equal the sum of the variances?
Yes, if each pair of the $X_i$'s are uncorrelated, this is true.
See the explanation on Wikipedia | Does the variance of a sum equal the sum of the variances?
Yes, if each pair of the $X_i$'s are uncorrelated, this is true.
See the explanation on Wikipedia |
1,672 | Does the variance of a sum equal the sum of the variances? | I just want to add some steps in the very first equivalence of Macro's answer. Indeed I think it can be helpful to retrieve it directly from the usual definition of variance ${\rm var}(X)=E([X-E(X)]^2)$ from which in this particular case we should have:
$$ {\rm var} \left( \sum_{i=1}^{n} X_i \right) = E \left( \left[ \... | Does the variance of a sum equal the sum of the variances? | I just want to add some steps in the very first equivalence of Macro's answer. Indeed I think it can be helpful to retrieve it directly from the usual definition of variance ${\rm var}(X)=E([X-E(X)]^2 | Does the variance of a sum equal the sum of the variances?
I just want to add some steps in the very first equivalence of Macro's answer. Indeed I think it can be helpful to retrieve it directly from the usual definition of variance ${\rm var}(X)=E([X-E(X)]^2)$ from which in this particular case we should have:
$$ {\rm... | Does the variance of a sum equal the sum of the variances?
I just want to add some steps in the very first equivalence of Macro's answer. Indeed I think it can be helpful to retrieve it directly from the usual definition of variance ${\rm var}(X)=E([X-E(X)]^2 |
1,673 | Explain "Curse of dimensionality" to a child | Probably the kid will like to eat cookies, so let us assume that you have a whole truck with cookies having a different colour, a different shape, a different taste, a different price ...
If the kid has to choose but only take into account one characteristic e.g. the taste, then it has four possibilities: sweet, salt, ... | Explain "Curse of dimensionality" to a child | Probably the kid will like to eat cookies, so let us assume that you have a whole truck with cookies having a different colour, a different shape, a different taste, a different price ...
If the kid h | Explain "Curse of dimensionality" to a child
Probably the kid will like to eat cookies, so let us assume that you have a whole truck with cookies having a different colour, a different shape, a different taste, a different price ...
If the kid has to choose but only take into account one characteristic e.g. the taste, ... | Explain "Curse of dimensionality" to a child
Probably the kid will like to eat cookies, so let us assume that you have a whole truck with cookies having a different colour, a different shape, a different taste, a different price ...
If the kid h |
1,674 | Explain "Curse of dimensionality" to a child | The analogy I like to use for the curse of dimensionality is a bit more on the geometric side, but I hope it's still sufficiently useful for your kid.
It's easy to hunt a dog and maybe catch it if it were running around on the plain (two dimensions). It's much harder to hunt birds, which now have an extra dimension the... | Explain "Curse of dimensionality" to a child | The analogy I like to use for the curse of dimensionality is a bit more on the geometric side, but I hope it's still sufficiently useful for your kid.
It's easy to hunt a dog and maybe catch it if it | Explain "Curse of dimensionality" to a child
The analogy I like to use for the curse of dimensionality is a bit more on the geometric side, but I hope it's still sufficiently useful for your kid.
It's easy to hunt a dog and maybe catch it if it were running around on the plain (two dimensions). It's much harder to hunt... | Explain "Curse of dimensionality" to a child
The analogy I like to use for the curse of dimensionality is a bit more on the geometric side, but I hope it's still sufficiently useful for your kid.
It's easy to hunt a dog and maybe catch it if it |
1,675 | Explain "Curse of dimensionality" to a child | Ok, so let's analyze the example of the child clustering its toys.
Imagine the child has only 3 toys:
a blue soccer ball
a blue freesbe
a green cube (ok maybe it's not the most fun toy you can imagine)
Let's do the following initial hypothesis regarding how a toy can be made:
Possible colors are: red, green, blue
Po... | Explain "Curse of dimensionality" to a child | Ok, so let's analyze the example of the child clustering its toys.
Imagine the child has only 3 toys:
a blue soccer ball
a blue freesbe
a green cube (ok maybe it's not the most fun toy you can imagin | Explain "Curse of dimensionality" to a child
Ok, so let's analyze the example of the child clustering its toys.
Imagine the child has only 3 toys:
a blue soccer ball
a blue freesbe
a green cube (ok maybe it's not the most fun toy you can imagine)
Let's do the following initial hypothesis regarding how a toy can be ma... | Explain "Curse of dimensionality" to a child
Ok, so let's analyze the example of the child clustering its toys.
Imagine the child has only 3 toys:
a blue soccer ball
a blue freesbe
a green cube (ok maybe it's not the most fun toy you can imagin |
1,676 | Explain "Curse of dimensionality" to a child | I have come across the following link that provides a very intuitive (and detailed) explanation of curse of dimensionality:
http://www.visiondummy.com/2014/04/curse-dimensionality-affect-classification/
In this article, we will discuss the so called ‘Curse of Dimensionality’, and explain why it is important when desig... | Explain "Curse of dimensionality" to a child | I have come across the following link that provides a very intuitive (and detailed) explanation of curse of dimensionality:
http://www.visiondummy.com/2014/04/curse-dimensionality-affect-classificatio | Explain "Curse of dimensionality" to a child
I have come across the following link that provides a very intuitive (and detailed) explanation of curse of dimensionality:
http://www.visiondummy.com/2014/04/curse-dimensionality-affect-classification/
In this article, we will discuss the so called ‘Curse of Dimensionality... | Explain "Curse of dimensionality" to a child
I have come across the following link that provides a very intuitive (and detailed) explanation of curse of dimensionality:
http://www.visiondummy.com/2014/04/curse-dimensionality-affect-classificatio |
1,677 | Explain "Curse of dimensionality" to a child | The curse of dimensionality is somewhat fuzzy in definition as it describes different but related things in different disciplines. The following illustrates machine learning’s curse of dimensionality:
Suppose a girl has ten toys, of which she likes only those in italics:
a brown teddy bear
a blue car
a red train
a yel... | Explain "Curse of dimensionality" to a child | The curse of dimensionality is somewhat fuzzy in definition as it describes different but related things in different disciplines. The following illustrates machine learning’s curse of dimensionality: | Explain "Curse of dimensionality" to a child
The curse of dimensionality is somewhat fuzzy in definition as it describes different but related things in different disciplines. The following illustrates machine learning’s curse of dimensionality:
Suppose a girl has ten toys, of which she likes only those in italics:
a ... | Explain "Curse of dimensionality" to a child
The curse of dimensionality is somewhat fuzzy in definition as it describes different but related things in different disciplines. The following illustrates machine learning’s curse of dimensionality: |
1,678 | Explain "Curse of dimensionality" to a child | Think of a circle enclosed in unit square.
Think of a sphere enclosed in the unit cube.
Think of an n-dimensional hyper sphere enclosed in the n-dimensional unit hyper cube.
The volume of the hyper cube is 1, of course, when measured in $1^n$ units. However, the volume of a hyper sphere shrinks with n growing.
If ther... | Explain "Curse of dimensionality" to a child | Think of a circle enclosed in unit square.
Think of a sphere enclosed in the unit cube.
Think of an n-dimensional hyper sphere enclosed in the n-dimensional unit hyper cube.
The volume of the hyper c | Explain "Curse of dimensionality" to a child
Think of a circle enclosed in unit square.
Think of a sphere enclosed in the unit cube.
Think of an n-dimensional hyper sphere enclosed in the n-dimensional unit hyper cube.
The volume of the hyper cube is 1, of course, when measured in $1^n$ units. However, the volume of a... | Explain "Curse of dimensionality" to a child
Think of a circle enclosed in unit square.
Think of a sphere enclosed in the unit cube.
Think of an n-dimensional hyper sphere enclosed in the n-dimensional unit hyper cube.
The volume of the hyper c |
1,679 | Explain "Curse of dimensionality" to a child | Me: "I am thinking of a small brown animal beginning with 'S'. What is it?"
Her: "Squirrel!"
Me: "OK, a harder one. I am thinking of a small brown animal. What is it?"
Her: "Still a squirrel?"
Me: "No"
Her: "Rat, mouse, vole?
Me: "Nope"
Her: "Umm... give me a clue"
Me: "Nope, but I'll do some thing better: I'll let y... | Explain "Curse of dimensionality" to a child | Me: "I am thinking of a small brown animal beginning with 'S'. What is it?"
Her: "Squirrel!"
Me: "OK, a harder one. I am thinking of a small brown animal. What is it?"
Her: "Still a squirrel?"
Me: " | Explain "Curse of dimensionality" to a child
Me: "I am thinking of a small brown animal beginning with 'S'. What is it?"
Her: "Squirrel!"
Me: "OK, a harder one. I am thinking of a small brown animal. What is it?"
Her: "Still a squirrel?"
Me: "No"
Her: "Rat, mouse, vole?
Me: "Nope"
Her: "Umm... give me a clue"
Me: "No... | Explain "Curse of dimensionality" to a child
Me: "I am thinking of a small brown animal beginning with 'S'. What is it?"
Her: "Squirrel!"
Me: "OK, a harder one. I am thinking of a small brown animal. What is it?"
Her: "Still a squirrel?"
Me: " |
1,680 | Explain "Curse of dimensionality" to a child | Suppose you want to ship some goods. You want to waste as little space as possible when packaging the goods (i.e., leave as little empty space as possible), because shipping costs are related to volume of the envelope/box. The containers at your disposal (envelopes, boxes) have right angles, so no sacks etc.
First prob... | Explain "Curse of dimensionality" to a child | Suppose you want to ship some goods. You want to waste as little space as possible when packaging the goods (i.e., leave as little empty space as possible), because shipping costs are related to volum | Explain "Curse of dimensionality" to a child
Suppose you want to ship some goods. You want to waste as little space as possible when packaging the goods (i.e., leave as little empty space as possible), because shipping costs are related to volume of the envelope/box. The containers at your disposal (envelopes, boxes) h... | Explain "Curse of dimensionality" to a child
Suppose you want to ship some goods. You want to waste as little space as possible when packaging the goods (i.e., leave as little empty space as possible), because shipping costs are related to volum |
1,681 | Explain "Curse of dimensionality" to a child | My 6 yo is more on the verse of the primary cause research, like in "but where did all this gas in the universe come from?"... well, I’ll imagine your child understand "higher dimensions", which seems very unlikely to me.
Let’s ask the following question: pick random points (uniformly) in a $n$-cube $[0,1]^n$, one by o... | Explain "Curse of dimensionality" to a child | My 6 yo is more on the verse of the primary cause research, like in "but where did all this gas in the universe come from?"... well, I’ll imagine your child understand "higher dimensions", which seems | Explain "Curse of dimensionality" to a child
My 6 yo is more on the verse of the primary cause research, like in "but where did all this gas in the universe come from?"... well, I’ll imagine your child understand "higher dimensions", which seems very unlikely to me.
Let’s ask the following question: pick random points ... | Explain "Curse of dimensionality" to a child
My 6 yo is more on the verse of the primary cause research, like in "but where did all this gas in the universe come from?"... well, I’ll imagine your child understand "higher dimensions", which seems |
1,682 | Explain "Curse of dimensionality" to a child | Fcop offered a great analogy with cookies but have covered only the sampling density aspect of the curse of dimensionality. We can extend this analogy to the sampling volume or the distance by distributing same number of Fcop's cookies in, say, ten boxes in one line, 10x10 boxes flat on the table and 10x10x10 in a stac... | Explain "Curse of dimensionality" to a child | Fcop offered a great analogy with cookies but have covered only the sampling density aspect of the curse of dimensionality. We can extend this analogy to the sampling volume or the distance by distrib | Explain "Curse of dimensionality" to a child
Fcop offered a great analogy with cookies but have covered only the sampling density aspect of the curse of dimensionality. We can extend this analogy to the sampling volume or the distance by distributing same number of Fcop's cookies in, say, ten boxes in one line, 10x10 b... | Explain "Curse of dimensionality" to a child
Fcop offered a great analogy with cookies but have covered only the sampling density aspect of the curse of dimensionality. We can extend this analogy to the sampling volume or the distance by distrib |
1,683 | Explain "Curse of dimensionality" to a child | There is a classic, textbook, math problem that shows this.
Would you rather earn (option 1) 100 pennies a day, every day for a month, or (option 2) a penny doubled every day for a month? You can ask your child this question.
If you choose option 1,
on day 1 you get 100 pennies
on day 2 you get 100 pennies
on day 3 yo... | Explain "Curse of dimensionality" to a child | There is a classic, textbook, math problem that shows this.
Would you rather earn (option 1) 100 pennies a day, every day for a month, or (option 2) a penny doubled every day for a month? You can ask | Explain "Curse of dimensionality" to a child
There is a classic, textbook, math problem that shows this.
Would you rather earn (option 1) 100 pennies a day, every day for a month, or (option 2) a penny doubled every day for a month? You can ask your child this question.
If you choose option 1,
on day 1 you get 100 pen... | Explain "Curse of dimensionality" to a child
There is a classic, textbook, math problem that shows this.
Would you rather earn (option 1) 100 pennies a day, every day for a month, or (option 2) a penny doubled every day for a month? You can ask |
1,684 | Explain "Curse of dimensionality" to a child | Think about cookie attributes (i.e. features, i.e. dimensions) and assume you want to find the one best cookie attributes combination.
With increasing features (dimensions) randomly picking the right becomes improbable. Put in another way: randomly picking a small interval on a line (1D) is easier than picking a small... | Explain "Curse of dimensionality" to a child | Think about cookie attributes (i.e. features, i.e. dimensions) and assume you want to find the one best cookie attributes combination.
With increasing features (dimensions) randomly picking the right | Explain "Curse of dimensionality" to a child
Think about cookie attributes (i.e. features, i.e. dimensions) and assume you want to find the one best cookie attributes combination.
With increasing features (dimensions) randomly picking the right becomes improbable. Put in another way: randomly picking a small interval ... | Explain "Curse of dimensionality" to a child
Think about cookie attributes (i.e. features, i.e. dimensions) and assume you want to find the one best cookie attributes combination.
With increasing features (dimensions) randomly picking the right |
1,685 | Simple algorithm for online outlier detection of a generic time series | Here is a simple R function that will find time series outliers (and optionally show them in a plot). It will handle seasonal and non-seasonal time series. The basic idea is to find robust estimates of the trend and seasonal components and subtract them. Then find outliers in the residuals. The test for residual outlie... | Simple algorithm for online outlier detection of a generic time series | Here is a simple R function that will find time series outliers (and optionally show them in a plot). It will handle seasonal and non-seasonal time series. The basic idea is to find robust estimates o | Simple algorithm for online outlier detection of a generic time series
Here is a simple R function that will find time series outliers (and optionally show them in a plot). It will handle seasonal and non-seasonal time series. The basic idea is to find robust estimates of the trend and seasonal components and subtract ... | Simple algorithm for online outlier detection of a generic time series
Here is a simple R function that will find time series outliers (and optionally show them in a plot). It will handle seasonal and non-seasonal time series. The basic idea is to find robust estimates o |
1,686 | Simple algorithm for online outlier detection of a generic time series | A good solution will have several ingredients, including:
Use a resistant, moving window smooth to remove nonstationarity.
Re-express the original data so that the residuals with respect to the smooth are approximately symmetrically distributed. Given the nature of your data, it's likely that their square roots or lo... | Simple algorithm for online outlier detection of a generic time series | A good solution will have several ingredients, including:
Use a resistant, moving window smooth to remove nonstationarity.
Re-express the original data so that the residuals with respect to the smoot | Simple algorithm for online outlier detection of a generic time series
A good solution will have several ingredients, including:
Use a resistant, moving window smooth to remove nonstationarity.
Re-express the original data so that the residuals with respect to the smooth are approximately symmetrically distributed. G... | Simple algorithm for online outlier detection of a generic time series
A good solution will have several ingredients, including:
Use a resistant, moving window smooth to remove nonstationarity.
Re-express the original data so that the residuals with respect to the smoot |
1,687 | Simple algorithm for online outlier detection of a generic time series | (This answer responded to a duplicate (now closed) question at Detecting outstanding events, which presented some data in graphical form.)
Outlier detection depends on the nature of the data and on what you are willing to assume about them. General-purpose methods rely on robust statistics. The spirit of this approa... | Simple algorithm for online outlier detection of a generic time series | (This answer responded to a duplicate (now closed) question at Detecting outstanding events, which presented some data in graphical form.)
Outlier detection depends on the nature of the data and on w | Simple algorithm for online outlier detection of a generic time series
(This answer responded to a duplicate (now closed) question at Detecting outstanding events, which presented some data in graphical form.)
Outlier detection depends on the nature of the data and on what you are willing to assume about them. Genera... | Simple algorithm for online outlier detection of a generic time series
(This answer responded to a duplicate (now closed) question at Detecting outstanding events, which presented some data in graphical form.)
Outlier detection depends on the nature of the data and on w |
1,688 | Simple algorithm for online outlier detection of a generic time series | If you're worried about assumptions with any particular approach, one approach is to train a number of learners on different signals, then use ensemble methods and aggregate over the "votes" from your learners to make the outlier classification.
BTW, this might be worth reading or skimming since it references a few app... | Simple algorithm for online outlier detection of a generic time series | If you're worried about assumptions with any particular approach, one approach is to train a number of learners on different signals, then use ensemble methods and aggregate over the "votes" from your | Simple algorithm for online outlier detection of a generic time series
If you're worried about assumptions with any particular approach, one approach is to train a number of learners on different signals, then use ensemble methods and aggregate over the "votes" from your learners to make the outlier classification.
BTW... | Simple algorithm for online outlier detection of a generic time series
If you're worried about assumptions with any particular approach, one approach is to train a number of learners on different signals, then use ensemble methods and aggregate over the "votes" from your |
1,689 | Simple algorithm for online outlier detection of a generic time series | I am guessing sophisticated time series model will not work for you because of the time it takes to detect outliers using this methodology. Therefore, here is a workaround:
First establish a baseline 'normal' traffic patterns for a year based on manual analysis of historical data which accounts for time of the day, w... | Simple algorithm for online outlier detection of a generic time series | I am guessing sophisticated time series model will not work for you because of the time it takes to detect outliers using this methodology. Therefore, here is a workaround:
First establish a baselin | Simple algorithm for online outlier detection of a generic time series
I am guessing sophisticated time series model will not work for you because of the time it takes to detect outliers using this methodology. Therefore, here is a workaround:
First establish a baseline 'normal' traffic patterns for a year based on m... | Simple algorithm for online outlier detection of a generic time series
I am guessing sophisticated time series model will not work for you because of the time it takes to detect outliers using this methodology. Therefore, here is a workaround:
First establish a baselin |
1,690 | Simple algorithm for online outlier detection of a generic time series | Seasonally adjust the data such that a normal day looks closer to flat. You could take today's 5:00pm sample and subtract or divide out the average of the previous 30 days at 5:00pm. Then look past N standard deviations (measured using pre-adjusted data) for outliers.
This could be done separately for weekly and daily... | Simple algorithm for online outlier detection of a generic time series | Seasonally adjust the data such that a normal day looks closer to flat. You could take today's 5:00pm sample and subtract or divide out the average of the previous 30 days at 5:00pm. Then look past N | Simple algorithm for online outlier detection of a generic time series
Seasonally adjust the data such that a normal day looks closer to flat. You could take today's 5:00pm sample and subtract or divide out the average of the previous 30 days at 5:00pm. Then look past N standard deviations (measured using pre-adjusted ... | Simple algorithm for online outlier detection of a generic time series
Seasonally adjust the data such that a normal day looks closer to flat. You could take today's 5:00pm sample and subtract or divide out the average of the previous 30 days at 5:00pm. Then look past N |
1,691 | Simple algorithm for online outlier detection of a generic time series | An alternative to the approach outlined by Rob Hyndman would be to use Holt-Winters Forecasting . The confidence bands derived from Holt-Winters can be used to detect outliers. Here is a paper that describes how to use Holt-Winters for "Aberrant Behavior Detection in Time Series for Network Monitoring". An implementati... | Simple algorithm for online outlier detection of a generic time series | An alternative to the approach outlined by Rob Hyndman would be to use Holt-Winters Forecasting . The confidence bands derived from Holt-Winters can be used to detect outliers. Here is a paper that de | Simple algorithm for online outlier detection of a generic time series
An alternative to the approach outlined by Rob Hyndman would be to use Holt-Winters Forecasting . The confidence bands derived from Holt-Winters can be used to detect outliers. Here is a paper that describes how to use Holt-Winters for "Aberrant Beh... | Simple algorithm for online outlier detection of a generic time series
An alternative to the approach outlined by Rob Hyndman would be to use Holt-Winters Forecasting . The confidence bands derived from Holt-Winters can be used to detect outliers. Here is a paper that de |
1,692 | Simple algorithm for online outlier detection of a generic time series | Spectral analysis detects periodicity in stationary time series. The frequency domain approach based on spectral density estimation is an approach I would recommend as your first step.
If for certain periods irregularity means a much higher peak than is typical for that period then the series with such irregularities ... | Simple algorithm for online outlier detection of a generic time series | Spectral analysis detects periodicity in stationary time series. The frequency domain approach based on spectral density estimation is an approach I would recommend as your first step.
If for certain | Simple algorithm for online outlier detection of a generic time series
Spectral analysis detects periodicity in stationary time series. The frequency domain approach based on spectral density estimation is an approach I would recommend as your first step.
If for certain periods irregularity means a much higher peak th... | Simple algorithm for online outlier detection of a generic time series
Spectral analysis detects periodicity in stationary time series. The frequency domain approach based on spectral density estimation is an approach I would recommend as your first step.
If for certain |
1,693 | Simple algorithm for online outlier detection of a generic time series | Since it is a time series data, a simple exponential filter http://en.wikipedia.org/wiki/Exponential_smoothing will smoothen the data. It is a very good filter since you don't need to accumulate old data points. Compare every newly smoothed data value with its unsmoothed value. Once the deviation exceeds a certain pre... | Simple algorithm for online outlier detection of a generic time series | Since it is a time series data, a simple exponential filter http://en.wikipedia.org/wiki/Exponential_smoothing will smoothen the data. It is a very good filter since you don't need to accumulate old | Simple algorithm for online outlier detection of a generic time series
Since it is a time series data, a simple exponential filter http://en.wikipedia.org/wiki/Exponential_smoothing will smoothen the data. It is a very good filter since you don't need to accumulate old data points. Compare every newly smoothed data va... | Simple algorithm for online outlier detection of a generic time series
Since it is a time series data, a simple exponential filter http://en.wikipedia.org/wiki/Exponential_smoothing will smoothen the data. It is a very good filter since you don't need to accumulate old |
1,694 | Simple algorithm for online outlier detection of a generic time series | You could use the standard deviation of the last N measurements (you have to pick a suitable N). A good anomaly score would be how many standard deviations a measurement is from the moving average. | Simple algorithm for online outlier detection of a generic time series | You could use the standard deviation of the last N measurements (you have to pick a suitable N). A good anomaly score would be how many standard deviations a measurement is from the moving average. | Simple algorithm for online outlier detection of a generic time series
You could use the standard deviation of the last N measurements (you have to pick a suitable N). A good anomaly score would be how many standard deviations a measurement is from the moving average. | Simple algorithm for online outlier detection of a generic time series
You could use the standard deviation of the last N measurements (you have to pick a suitable N). A good anomaly score would be how many standard deviations a measurement is from the moving average. |
1,695 | Simple algorithm for online outlier detection of a generic time series | what I do is group the measurements by hour and day of week and compare standard deviations of that. Still doesn't correct for things like holidays and summer/winter seasonality but its correct most of the time.
The downside is that you really need to collect a year or so of data to have enough so that stddev starts ma... | Simple algorithm for online outlier detection of a generic time series | what I do is group the measurements by hour and day of week and compare standard deviations of that. Still doesn't correct for things like holidays and summer/winter seasonality but its correct most o | Simple algorithm for online outlier detection of a generic time series
what I do is group the measurements by hour and day of week and compare standard deviations of that. Still doesn't correct for things like holidays and summer/winter seasonality but its correct most of the time.
The downside is that you really need ... | Simple algorithm for online outlier detection of a generic time series
what I do is group the measurements by hour and day of week and compare standard deviations of that. Still doesn't correct for things like holidays and summer/winter seasonality but its correct most o |
1,696 | Simple algorithm for online outlier detection of a generic time series | anomaly detection requires the construction of an equation which describes the expectation. Intervention Detection is available in both a non-causal and causal setting . If one has a predictor series like price then things can get a little complicated. Other responses here don't seem to take into account assignable cau... | Simple algorithm for online outlier detection of a generic time series | anomaly detection requires the construction of an equation which describes the expectation. Intervention Detection is available in both a non-causal and causal setting . If one has a predictor series | Simple algorithm for online outlier detection of a generic time series
anomaly detection requires the construction of an equation which describes the expectation. Intervention Detection is available in both a non-causal and causal setting . If one has a predictor series like price then things can get a little complicat... | Simple algorithm for online outlier detection of a generic time series
anomaly detection requires the construction of an equation which describes the expectation. Intervention Detection is available in both a non-causal and causal setting . If one has a predictor series |
1,697 | Simple algorithm for online outlier detection of a generic time series | For the case where one has to compute the outliers quickly, one could use the idea of Rob Hyndman and Mahito Sugiyama ( https://github.com/BorgwardtLab/sampling-outlier-detection , library(spoutlier), function qsp ) to compute the outliers as follows:
library(spoutlier)
rapidtsoutliers <- function(x,plot=FALSE,seed=123... | Simple algorithm for online outlier detection of a generic time series | For the case where one has to compute the outliers quickly, one could use the idea of Rob Hyndman and Mahito Sugiyama ( https://github.com/BorgwardtLab/sampling-outlier-detection , library(spoutlier), | Simple algorithm for online outlier detection of a generic time series
For the case where one has to compute the outliers quickly, one could use the idea of Rob Hyndman and Mahito Sugiyama ( https://github.com/BorgwardtLab/sampling-outlier-detection , library(spoutlier), function qsp ) to compute the outliers as follow... | Simple algorithm for online outlier detection of a generic time series
For the case where one has to compute the outliers quickly, one could use the idea of Rob Hyndman and Mahito Sugiyama ( https://github.com/BorgwardtLab/sampling-outlier-detection , library(spoutlier), |
1,698 | US Election results 2016: What went wrong with prediction models? | In short, polling is not always easy. This election may have been the hardest.
Any time we are trying to do statistical inference, a fundamental question is whether our sample is a good representation of the population of interest. A typical assumption that is required for many types of statistical inference is that o... | US Election results 2016: What went wrong with prediction models? | In short, polling is not always easy. This election may have been the hardest.
Any time we are trying to do statistical inference, a fundamental question is whether our sample is a good representatio | US Election results 2016: What went wrong with prediction models?
In short, polling is not always easy. This election may have been the hardest.
Any time we are trying to do statistical inference, a fundamental question is whether our sample is a good representation of the population of interest. A typical assumption ... | US Election results 2016: What went wrong with prediction models?
In short, polling is not always easy. This election may have been the hardest.
Any time we are trying to do statistical inference, a fundamental question is whether our sample is a good representatio |
1,699 | US Election results 2016: What went wrong with prediction models? | There are a number of sources of polling error:
You find some people hard to reach
This is corrected by doing demographic analysis, then correcting for your sampling bias. If your demographic analysis doesn't reflect the things that make people hard to reach, this correction does not repair the damage.
People lie
You... | US Election results 2016: What went wrong with prediction models? | There are a number of sources of polling error:
You find some people hard to reach
This is corrected by doing demographic analysis, then correcting for your sampling bias. If your demographic analys | US Election results 2016: What went wrong with prediction models?
There are a number of sources of polling error:
You find some people hard to reach
This is corrected by doing demographic analysis, then correcting for your sampling bias. If your demographic analysis doesn't reflect the things that make people hard to... | US Election results 2016: What went wrong with prediction models?
There are a number of sources of polling error:
You find some people hard to reach
This is corrected by doing demographic analysis, then correcting for your sampling bias. If your demographic analys |
1,700 | US Election results 2016: What went wrong with prediction models? | This was mentioned in the comments on the accepted answer (hat-tip to Mehrdad), but I think it should be emphasized. 538 actually did this quite well this cycle*.
538 is a polling aggregator that runs models against each state to try to predict the winner. Their final run gave Trump about a 30% chance of winning. That ... | US Election results 2016: What went wrong with prediction models? | This was mentioned in the comments on the accepted answer (hat-tip to Mehrdad), but I think it should be emphasized. 538 actually did this quite well this cycle*.
538 is a polling aggregator that runs | US Election results 2016: What went wrong with prediction models?
This was mentioned in the comments on the accepted answer (hat-tip to Mehrdad), but I think it should be emphasized. 538 actually did this quite well this cycle*.
538 is a polling aggregator that runs models against each state to try to predict the winne... | US Election results 2016: What went wrong with prediction models?
This was mentioned in the comments on the accepted answer (hat-tip to Mehrdad), but I think it should be emphasized. 538 actually did this quite well this cycle*.
538 is a polling aggregator that runs |
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