Is dimension reduction helpful to select features for a classification problem?Dimension reduction for logical arraysVarious algorithms performance in a problem and what can be deduced about data and problem?Deciding about dimensionality reduction, classification and clustering?Can I apply Clustering algorithms to the result of Manifold Visualization Methods?Python: Handling imbalance Classes in python Machine LearningDimension Reduction - After or Before Train-Test Splitselecting variable randomly at each node in a tree in Random ForestWhy are autoencoders for dimension reduction symmetrical?How to select features for Text classification problemPCA, SMOTE and cross validation- how to combine them together?
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Is dimension reduction helpful to select features for a classification problem?
Dimension reduction for logical arraysVarious algorithms performance in a problem and what can be deduced about data and problem?Deciding about dimensionality reduction, classification and clustering?Can I apply Clustering algorithms to the result of Manifold Visualization Methods?Python: Handling imbalance Classes in python Machine LearningDimension Reduction - After or Before Train-Test Splitselecting variable randomly at each node in a tree in Random ForestWhy are autoencoders for dimension reduction symmetrical?How to select features for Text classification problemPCA, SMOTE and cross validation- how to combine them together?
$begingroup$
Let's say I have a data set but I don't know what features are relevant to solve a classification/regression problem.
In this case, is it worth/good to use a dimension reduction algorithm and then apply a classification algorithm ? Or can I just select "randomly" my features by using my common sense and then try to tune my algorithm next ?
Also if someone have some explanation of a dimension reduction "in real life with real use case" it would be great because I feel my comprehension of dimension reduction is wrong !
classification data-mining dimensionality-reduction
asked Feb 20 at 23:02
FK IEFK IE
162
$endgroup$
bumped to the homepage by Community♦ 49 secs ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
Let's say I have a data set but I don't know what features are relevant to solve a classification/regression problem.
In this case, is it worth/good to use a dimension reduction algorithm and then apply a classification algorithm ? Or can I just select "randomly" my features by using my common sense and then try to tune my algorithm next ?
Also if someone have some explanation of a dimension reduction "in real life with real use case" it would be great because I feel my comprehension of dimension reduction is wrong !
classification data-mining dimensionality-reduction
asked Feb 20 at 23:02
FK IEFK IE
162
$endgroup$
bumped to the homepage by Community♦ 49 secs ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
Let's say I have a data set but I don't know what features are relevant to solve a classification/regression problem.
In this case, is it worth/good to use a dimension reduction algorithm and then apply a classification algorithm ? Or can I just select "randomly" my features by using my common sense and then try to tune my algorithm next ?
Also if someone have some explanation of a dimension reduction "in real life with real use case" it would be great because I feel my comprehension of dimension reduction is wrong !
classification data-mining dimensionality-reduction
asked Feb 20 at 23:02
FK IEFK IE
162
$endgroup$
Let's say I have a data set but I don't know what features are relevant to solve a classification/regression problem.
In this case, is it worth/good to use a dimension reduction algorithm and then apply a classification algorithm ? Or can I just select "randomly" my features by using my common sense and then try to tune my algorithm next ?
Also if someone have some explanation of a dimension reduction "in real life with real use case" it would be great because I feel my comprehension of dimension reduction is wrong !
classification data-mining dimensionality-reduction
classification data-mining dimensionality-reduction
asked Feb 20 at 23:02
FK IEFK IE
162
asked Feb 20 at 23:02
FK IEFK IE
162
asked Feb 20 at 23:02
FK IEFK IE
162
asked Feb 20 at 23:02
FK IEFK IE
162
asked Feb 20 at 23:02
FK IEFK IE
162
162
bumped to the homepage by Community♦ 49 secs ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 49 secs ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
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$begingroup$
If you don't care which features are included, using PCA (or something similar) can help.
If you do have some information on which features influence classification or regression, you can certainly try to fit a model without dimensional reduction.
PCA, which is one of the more common dimensional reduction techniques, yields vectors that are all orthogonal (as in, uncorrelated). This means that even if your features are correlated, after the dimensional reduction, your model won't struggle with collinearity. Depending on your model type, this can be crucial. A real life example could be any housing dataset, where the features describe the house and the target is the price. Many of the features will be correlated (e.g. number of bathrooms and number of bedroom or number of rooms and square footage), and so a linear regression model may get tripped up by the collinearity. Dimensional reduction will capture the variance across the features while yielding fewer columns.
answered Feb 21 at 1:13
David AtlasDavid Atlas
312
$endgroup$
add a comment |
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1 Answer
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$begingroup$
If you don't care which features are included, using PCA (or something similar) can help.
If you do have some information on which features influence classification or regression, you can certainly try to fit a model without dimensional reduction.
PCA, which is one of the more common dimensional reduction techniques, yields vectors that are all orthogonal (as in, uncorrelated). This means that even if your features are correlated, after the dimensional reduction, your model won't struggle with collinearity. Depending on your model type, this can be crucial. A real life example could be any housing dataset, where the features describe the house and the target is the price. Many of the features will be correlated (e.g. number of bathrooms and number of bedroom or number of rooms and square footage), and so a linear regression model may get tripped up by the collinearity. Dimensional reduction will capture the variance across the features while yielding fewer columns.
answered Feb 21 at 1:13
David AtlasDavid Atlas
312
$endgroup$
add a comment |
$begingroup$
If you don't care which features are included, using PCA (or something similar) can help.
If you do have some information on which features influence classification or regression, you can certainly try to fit a model without dimensional reduction.
PCA, which is one of the more common dimensional reduction techniques, yields vectors that are all orthogonal (as in, uncorrelated). This means that even if your features are correlated, after the dimensional reduction, your model won't struggle with collinearity. Depending on your model type, this can be crucial. A real life example could be any housing dataset, where the features describe the house and the target is the price. Many of the features will be correlated (e.g. number of bathrooms and number of bedroom or number of rooms and square footage), and so a linear regression model may get tripped up by the collinearity. Dimensional reduction will capture the variance across the features while yielding fewer columns.
answered Feb 21 at 1:13
David AtlasDavid Atlas
312
$endgroup$
add a comment |
$begingroup$
If you don't care which features are included, using PCA (or something similar) can help.
If you do have some information on which features influence classification or regression, you can certainly try to fit a model without dimensional reduction.
PCA, which is one of the more common dimensional reduction techniques, yields vectors that are all orthogonal (as in, uncorrelated). This means that even if your features are correlated, after the dimensional reduction, your model won't struggle with collinearity. Depending on your model type, this can be crucial. A real life example could be any housing dataset, where the features describe the house and the target is the price. Many of the features will be correlated (e.g. number of bathrooms and number of bedroom or number of rooms and square footage), and so a linear regression model may get tripped up by the collinearity. Dimensional reduction will capture the variance across the features while yielding fewer columns.
answered Feb 21 at 1:13
David AtlasDavid Atlas
312
$endgroup$
If you don't care which features are included, using PCA (or something similar) can help.
If you do have some information on which features influence classification or regression, you can certainly try to fit a model without dimensional reduction.
PCA, which is one of the more common dimensional reduction techniques, yields vectors that are all orthogonal (as in, uncorrelated). This means that even if your features are correlated, after the dimensional reduction, your model won't struggle with collinearity. Depending on your model type, this can be crucial. A real life example could be any housing dataset, where the features describe the house and the target is the price. Many of the features will be correlated (e.g. number of bathrooms and number of bedroom or number of rooms and square footage), and so a linear regression model may get tripped up by the collinearity. Dimensional reduction will capture the variance across the features while yielding fewer columns.
answered Feb 21 at 1:13
David AtlasDavid Atlas
312
answered Feb 21 at 1:13
David AtlasDavid Atlas
312
answered Feb 21 at 1:13
David AtlasDavid Atlas
312
answered Feb 21 at 1:13
David AtlasDavid Atlas
312
312
add a comment |
add a comment |
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