A logistic regression model is fitted to the linear scores using lrm() function and the logistic scores are computed using the formula: 1/(1+exp(-(a+bX))) where a and b are the logistic coefficients.
If a subset of samples are selected randomly, the navigate of positive classes might be too sparse or even empty. This function will repeat sampling until the classes are appropriate in this sense.
Various linear models are fitted to the training samples using lars method. The models differ in the number of features and each is validated by validating samples. A score is also assigned to each feature based on the tendency of LASSO in including that feature in the models.
Description: For each feature, a score is computed that can be useful for feature selection. Several random subsets are sampled from the input data and for each random subset, various linear models are fitted using lars method. A score is assigned to each feature based on the tendency of LASSO in including that feature in the models.Finally, the average score and the models are returned as the output. The features with relatively low scores are recommended to be ignored because they can lead to overfitting of the model to the training data.Moreover, for each random subset, the best set of features in terms of global error is returned. They are useful for applying Bolasso, the alternative feature selection method that recommends the intersection of features subsets.
Several random subsets are sampled from the input data and for each random subset, various linear models are fitted using lars method. A score is assigned to each feature based on the tendency of LASSO in including that feature in the models. Finally, the average score and the models are returned as the output.
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