SKlearn Univariate feature selection if the features are continuous and output is categorical2019 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
2019 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 Mar 28 at 18:48
user70415user70415
11
$endgroup$
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 Mar 28 at 18:48
user70415user70415
11
$endgroup$
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 Mar 28 at 18:48
user70415user70415
11
$endgroup$
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 Mar 28 at 18:48
user70415user70415
11
asked Mar 28 at 18:48
user70415user70415
11
asked Mar 28 at 18:48
user70415user70415
11
asked Mar 28 at 18:48
user70415user70415
11
asked Mar 28 at 18:48
user70415user70415
11
11
add a comment |
add a comment |
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