Regarding your question about Python implementations of the given R examples:
SKlearn has ready to use implementations for feature selection as they were described under the linked question in R (see here).
Here is an example for categorical input and output data: With
SelectKBest you can select the K features with the highest corelation, e.g. based on a
chi squared test.
import numpy as np
from sklearn.datasets import load_iris
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
# load the famous iris data set for which X.shape is (150, 4) and y.shape (150,)
iris = load_iris()
X, y = iris.data, iris.target
# Add exponentially distributed noise (20 new attributes)
rng = np.random.RandomState()
noise = rng.exponential(size=(len(iris.data), 20))
# While X.shape is (150, 4), X_noisy has shape (150, 24)
X_noisy = np.hstack([iris.data, noise])
# Select 4 features based on chi squared test
selector = SelectKBest(chi2, k=4)
X_selected = selector.transform(X_noisy)
Checking the shapes gives the following:
Out: (150, 4)
Out: (150, 24)
Out: (150, 4)
You can also check which features were selected:
[False False True True False False False False False True False False
False False False False False False False False False False True False]
As you can see 2 of the initial 4 features (which were not noise) did get selected. The feature selectors also have attributes to check for examples the p values (see here for
The book 'Introduction to machine learning with Python' by Mueller and Guido has a section about it too (their example is very close the one above).
However, the example of a
CHI squared test I gave is applicable to categorical independent and dependent variables. For a mix of categorical and continuous independent variables you might need to discretize the continuous variables or check other methods for features selection, e.g. model-based.