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.
|Date of publication||2015-05-13 00:55:14|
|Maintainer||Habil Zare <email@example.com>|
|License||GPL (>= 2)|
compute.balanced: Balances between negative and positive samples by...
compute.logistic.score: Fits a logistic regression model using the linear scores
doctor.validate: Validates a model using validaing samples.
FeaLect: Computes the scores of the features.
FeaLect-package: Scores features for Feature seLection
ignore.redundant: Refines a feature matrix
input.check.FeaLect: Checks the inputs to Fealect() function.
mcl_sll: MCL and SLL lymphoma subtypes
random.subset: Selects a random subset of the input.
train.doctor: Fittes various models based on a combination on penalized...