Yes, you will have to convert everything to numbers. That requires thinking about what these attributes represent. Usually there are three possibilities: One-Hot encoding for categorical data Arbitrary numbers for ordinal data Use something like group means for categorical data (e. g. mean prices for city districts). You have to be carefull to not infuse … Read more
This doesn’t require loops. # Create and fit selector selector = SelectKBest(f_classif, k=5) selector.fit(features_df, target) # Get columns to keep and create new dataframe with those only cols_idxs = selector.get_support(indices=True) features_df_new = features_df.iloc[:,cols_idxs]
There are indeed several ways to get feature “importances”. As often, there is no strict consensus about what this word means. In scikit-learn, we implement the importance as described in [1] (often cited, but unfortunately rarely read…). It is sometimes called “gini importance” or “mean decrease impurity” and is defined as the total decrease in … Read more