Description Usage Arguments Value Examples
This is an implementation of the forward selection algorithm in which you start with a null model and iteratively add the most useful features. This function is built for the specific case of forward selection in linear regression.
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X_train |
Training data. Represented as a 2D matrix or dataframe of (observations, features). |
y_train |
Target class for training data. Represented as a 1D vector of target classes for |
X_val |
Validation data. Represented as a 2D matrix or dataframe of (observations, features). |
y_val |
Target class for validation data. Represented as a 1D vector of target classes for |
min_change |
The smallest change in criterion score to be considered significant.
Note: |
n_features |
The number of features to select, expressed either as a proportion (0,1)
or whole number with range (0,total_features). Note: |
criterion |
Model selection criterion to measure relative model quality. Can be one of:
|
verbose |
If |
A vector of indices that represent the best features of the model.
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