Regularization with Ridge & Lasso Regression using Python and SciKit Learn

Introduction:

Ridge and Lasso Regression are powerful techniques used in machine learning to prevent overfitting. Both techniques are forms of regularization, which involves adding a penalty term to the cost function to reduce the complexity of the model.

In this tutorial, we will learn how to implement Ridge and Lasso Regression using Python and the SciKit Learn library. We will also discuss the differences between the two methods and how to choose the appropriate regularization technique for your dataset.

Prerequisites:

Before we begin, make sure you have the following Python libraries installed: NumPy, SciKit Learn, and Pandas. You can install these libraries using pip:

pip install numpy
pip install scikit-learn
pip install pandas

Dataset:

For this tutorial, we will be using the Boston Housing dataset, which is included in the SciKit Learn library. This dataset contains information about housing prices in Boston based on various features such as crime rate, number of rooms, etc.

Step 1: Importing Libraries

First, let’s import the necessary libraries:

import numpy as np
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Ridge, Lasso
from sklearn.metrics import mean_squared_error

Step 2: Loading the Dataset

Next, let’s load the Boston Housing dataset and split it into training and testing sets:

boston = load_boston()
X = boston.data
y = boston.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Step 3: Ridge Regression

Ridge Regression adds a penalty term equal to the square of the magnitude of coefficients. Let’s create a Ridge regression model and fit it to our training data:

ridge = Ridge(alpha=1.0)
ridge.fit(X_train, y_train)

Step 4: Lasso Regression

Lasso Regression adds a penalty term equal to the absolute value of the magnitude of coefficients. Let’s create a Lasso regression model and fit it to our training data:

lasso = Lasso(alpha=1.0)
lasso.fit(X_train, y_train)

Step 5: Evaluating the Models

Now that we have trained our Ridge and Lasso regression models, let’s evaluate their performance on the testing set:

ridge_pred = ridge.predict(X_test)
lasso_pred = lasso.predict(X_test)

ridge_mse = mean_squared_error(y_test, ridge_pred)
lasso_mse = mean_squared_error(y_test, lasso_pred)

print("Ridge Regression Mean Squared Error:", ridge_mse)
print("Lasso Regression Mean Squared Error:", lasso_mse)

Step 6: Choosing the Right Regularization Technique

Ridge and Lasso Regression both work by penalizing the coefficients of the model to prevent overfitting. The main difference between the two techniques is the type of penalty term used. Ridge Regression tends to shrink the coefficients towards zero, while Lasso Regression tends to shrink some coefficients to exactly zero.

In general, if you have a large number of features and suspect that only a few of them are important, Lasso Regression may be more suitable. On the other hand, if you have a small number of features and suspect that all of them are important, Ridge Regression may be a better choice.

Conclusion:

In this tutorial, we learned how to implement Ridge and Lasso Regression using Python and the SciKit Learn library. We also discussed the differences between the two techniques and how to choose the appropriate regularization technique for your dataset.

Regularization is an important tool in machine learning to prevent overfitting and improve the generalization of the model. By using Ridge and Lasso Regression, you can create more robust and accurate machine learning models.