Stepwise Regression

The stepwise regression technique is used while dealing with more than one independent variable. These variables get chosen using an automatic process without any human intervention. This is easily achievable by being observant on statistical values such as R-square, AIC metrics, and t-stats to recognize significant variables.

Scikit-learn does not have a specific implementation of stepwise regression. However, there are a few ways to perform stepwise regression in sklearn using other modules.

One way is to use the SelectKBest selector. This selector takes a scoring function as input and returns the K best features based on their scores. To perform stepwise regression, you can start with a large value of K and then iteratively reduce K and re-fit your model until you reach a satisfactory level of performance.

Another way to perform stepwise regression in sklearn is to use the RFE selector. This selector recursively eliminates features until the specified number of features remain. To perform stepwise regression, you can start with a large number of features and then iteratively eliminate features using RFE and re-fit your model until you reach a satisfactory level of performance.

Here is an example of how to perform stepwise regression in sklearn using the SelectKBest selector:

import numpy as np
import pandas as pd 
from sklearn.feature_selection import SelectKBest 
from sklearn.linear_model import LinearRegression 
# Load the data 
df = pd.read_csv('data.csv') 
# Define the target variable 
y = df['target'] 
# Define the feature variables
 X = df.drop('target', axis=1) 
# Instantiate the SelectKBest selector 
selector = SelectKBest(f_regression, k=10)
 # Fit the selector to the data 
selector.fit(X, y) 
# Get the selected features s
elected_features = selector.get_support(indices=True) 
# Create a new dataset with only the selected features
 X_selected = X[selected_features] 
# Instantiate and fit the linear regression model 
model = LinearRegression() 
model.fit(X_selected, y)

References

1-Top 6 Regression Techniques a Data Science Specialist Needs to Know

 2-Introduction to Linear Regression - mathematics and application with Python