SKlearn Univariate feature selection if the features are continuous and output is categorical The Next CEO of Stack Overflow2019 Community Moderator ElectionHow to deal with categorical feature of very high cardinality?Preparing, Scaling and Selecting from a combination of numerical and categorical featuresChi square distribution for feature selectionfeature selection such that features that explain target but are not correlated with confounds are pickedHow to combine categorical and continuous input features for neural network trainingChi-squared for continuous variablesWhat's the best way to select features independent of the model being used?When to perform feature selection, how, and how does data affect choosing the predictive model?Feature selection/reduction techinique for combination of features in image processingFeature selection with information gain (KL divergence) and mutual information yields different results
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SKlearn Univariate feature selection if the features are continuous and output is categorical
The Next CEO of Stack Overflow2019 Community Moderator ElectionHow to deal with categorical feature of very high cardinality?Preparing, Scaling and Selecting from a combination of numerical and categorical featuresChi square distribution for feature selectionfeature selection such that features that explain target but are not correlated with confounds are pickedHow to combine categorical and continuous input features for neural network trainingChi-squared for continuous variablesWhat's the best way to select features independent of the model being used?When to perform feature selection, how, and how does data affect choosing the predictive model?Feature selection/reduction techinique for combination of features in image processingFeature selection with information gain (KL divergence) and mutual information yields different results
$begingroup$
I have a dataset with 200 numerical features, but the target is binary (0 or 1). If I use uni-variate feature selection,
What is the right scoring parameter (f_regression or f_classif /chi square)?
What is a good rule of thumb to follow in such cases where the target is continuous/categorical and but the features are different (i.e. categorical/continuous)?
scikit-learn feature-selection feature-extraction
asked 45 mins ago
user70415user70415
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New contributor
user70415 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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$endgroup$
add a comment |
$begingroup$
I have a dataset with 200 numerical features, but the target is binary (0 or 1). If I use uni-variate feature selection,
What is the right scoring parameter (f_regression or f_classif /chi square)?
What is a good rule of thumb to follow in such cases where the target is continuous/categorical and but the features are different (i.e. categorical/continuous)?
scikit-learn feature-selection feature-extraction
asked 45 mins ago
user70415user70415
1
New contributor
user70415 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I have a dataset with 200 numerical features, but the target is binary (0 or 1). If I use uni-variate feature selection,
What is the right scoring parameter (f_regression or f_classif /chi square)?
What is a good rule of thumb to follow in such cases where the target is continuous/categorical and but the features are different (i.e. categorical/continuous)?
scikit-learn feature-selection feature-extraction
asked 45 mins ago
user70415user70415
1
New contributor
user70415 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have a dataset with 200 numerical features, but the target is binary (0 or 1). If I use uni-variate feature selection,
What is the right scoring parameter (f_regression or f_classif /chi square)?
What is a good rule of thumb to follow in such cases where the target is continuous/categorical and but the features are different (i.e. categorical/continuous)?
scikit-learn feature-selection feature-extraction
scikit-learn feature-selection feature-extraction
asked 45 mins ago
user70415user70415
1
New contributor
user70415 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 45 mins ago
user70415user70415
1
New contributor
user70415 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 45 mins ago
user70415user70415
1
New contributor
user70415 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 45 mins ago
user70415user70415
1
asked 45 mins ago
user70415user70415
1
1
New contributor
user70415 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
user70415 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
user70415 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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add a comment |
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