query: Feature selection vs regulation in supervised learning
positive_passages.docid: 142959
positive_passages.text: Do we still need to do feature selection while using Regularization algorithms?
negative_passages.docid: 141640
negative_passages.text: Sklearn - Choosing the right model for supervised learning/classification task
negative_passages.docid: 14183
negative_passages.text: Why Lasso or ElasticNet perform better than Ridge when the features are correlated
negative_passages.docid: 57060
negative_passages.text: Is normalizing the features always good for classification?
negative_passages.docid: 65682
negative_passages.text: Why does universal generalization work? (the rule of inference)
negative_passages.docid: 117722
negative_passages.text: Interplay between early stopping and cross validation
negative_passages.docid: 27143
negative_passages.text: Detecting significant predictors out of many independent variables
negative_passages.docid: 112378
negative_passages.text: Decision boundaries and Gaussian density functions
negative_passages.docid: 26149
negative_passages.text: Logistic regression vs. LDA as two-class classifiers
negative_passages.docid: 3013
negative_passages.text: Why does the Lasso provide Variable Selection?
negative_passages.docid: 84407
negative_passages.text: How to judge if a supervised machine learning model is overfitting or not?
negative_passages.docid: 91880
negative_passages.text: Suggestions for cost-sensitive learning in a highly imbalanced setting
negative_passages.docid: 153341
negative_passages.text: Lasso regression feature selection
negative_passages.docid: 44881
negative_passages.text: Using lasso for feature selection, followed by a non-regularized regression
negative_passages.docid: 74614
negative_passages.text: KKT versus unconstrained formulation of lasso regression
negative_passages.docid: 51084
negative_passages.text: Won't highly-correlated variables in random forest distort accuracy and feature-selection?
negative_passages.docid: 39736
negative_passages.text: Regularization methods for logistic regression