Can we use pca for supervise classification?Does high error rate in regression imply the data set is unpredictable?How to extract features and classify alert emails coming from monitoring tools into proper category?Online/incremental unsupervised dimensionality reduction for use with classification for event predictionUnsupervised Classification for documentsFeature reduction conveniencePreparing, Scaling and Selecting from a combination of numerical and categorical featuresPCA - Error minimization and Variance MaximizationWhen to perform feature selection, how, and how does data affect choosing the predictive model?Show importance of variables from a data set without a response variable? Use PCA?Does PCA decrease the feature on my Data set or just decrease the dimension?
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Can we use pca for supervise classification?
Does high error rate in regression imply the data set is unpredictable?How to extract features and classify alert emails coming from monitoring tools into proper category?Online/incremental unsupervised dimensionality reduction for use with classification for event predictionUnsupervised Classification for documentsFeature reduction conveniencePreparing, Scaling and Selecting from a combination of numerical and categorical featuresPCA - Error minimization and Variance MaximizationWhen to perform feature selection, how, and how does data affect choosing the predictive model?Show importance of variables from a data set without a response variable? Use PCA?Does PCA decrease the feature on my Data set or just decrease the dimension?
$begingroup$
My questions are:
- Can we use "pca feature selection" for supervised classification? What will happen to labels when we use dimension reduction?
- If I understand it right when we use pca for
feature extraction after do it we have old features but in new
dimension then can we say these are the new features or mixed
features? - Is "feature extraction" different with "feature
selection" in pca? I mean they are two things to do with different
ways. - How can select effective features with pca?
There is a lots of references on the internet but I couldn't find
any MATLAB sample code or video (on youtube) about pca feature
selection, I mean not in theory, I'm looking for sample code in any language(Paython, Matlab,etc).
classification feature-selection feature-extraction pca
New contributor
$endgroup$
add a comment |
$begingroup$
My questions are:
- Can we use "pca feature selection" for supervised classification? What will happen to labels when we use dimension reduction?
- If I understand it right when we use pca for
feature extraction after do it we have old features but in new
dimension then can we say these are the new features or mixed
features? - Is "feature extraction" different with "feature
selection" in pca? I mean they are two things to do with different
ways. - How can select effective features with pca?
There is a lots of references on the internet but I couldn't find
any MATLAB sample code or video (on youtube) about pca feature
selection, I mean not in theory, I'm looking for sample code in any language(Paython, Matlab,etc).
classification feature-selection feature-extraction pca
New contributor
$endgroup$
add a comment |
$begingroup$
My questions are:
- Can we use "pca feature selection" for supervised classification? What will happen to labels when we use dimension reduction?
- If I understand it right when we use pca for
feature extraction after do it we have old features but in new
dimension then can we say these are the new features or mixed
features? - Is "feature extraction" different with "feature
selection" in pca? I mean they are two things to do with different
ways. - How can select effective features with pca?
There is a lots of references on the internet but I couldn't find
any MATLAB sample code or video (on youtube) about pca feature
selection, I mean not in theory, I'm looking for sample code in any language(Paython, Matlab,etc).
classification feature-selection feature-extraction pca
New contributor
$endgroup$
My questions are:
- Can we use "pca feature selection" for supervised classification? What will happen to labels when we use dimension reduction?
- If I understand it right when we use pca for
feature extraction after do it we have old features but in new
dimension then can we say these are the new features or mixed
features? - Is "feature extraction" different with "feature
selection" in pca? I mean they are two things to do with different
ways. - How can select effective features with pca?
There is a lots of references on the internet but I couldn't find
any MATLAB sample code or video (on youtube) about pca feature
selection, I mean not in theory, I'm looking for sample code in any language(Paython, Matlab,etc).
classification feature-selection feature-extraction pca
classification feature-selection feature-extraction pca
New contributor
New contributor
New contributor
asked 33 mins ago
motevalizadehmotevalizadeh
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