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
It is not actually difficult to demonstrate why using the whole dataset (i.e. before splitting to train/test) for selecting features can lead you astray. Here is one such demonstration using random dummy data with Python and scikit-learn: import numpy as np from sklearn.feature_selection import SelectKBest from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics … Read more
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
First of all, I assume that you call features the variables and not the samples/observations. In this case, you could do something like the following by creating a biplot function that shows everything in one plot. In this example, I am using the iris data. Before the example, please note that the basic idea when … Read more