Lasso Regression with Scikit-Learn (Beginner Friendly)

When it comes to machine learning, regression is a commonly used technique for modeling the relationship between a dependent variable and one or more independent variables. Lasso regression is a type of linear regression that incorporates regularization to prevent overfitting.

Scikit-Learn is a popular machine learning library in Python that provides tools for building and evaluating machine learning models. In this article, we will explore how to perform Lasso regression using Scikit-Learn.

Installing Scikit-Learn

If you haven’t already, you can install Scikit-Learn using pip:

pip install scikit-learn

Importing the Necessary Libraries

Before we can start using Scikit-Learn for Lasso regression, we need to import the required libraries:


import numpy as np
import pandas as pd
from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error


Loading the Data

For this tutorial, let’s use a sample dataset to demonstrate Lasso regression. You can load your own dataset or use a built-in one from Scikit-Learn:


from sklearn.datasets import load_boston
data = load_boston()
X = data.data
y = data.target


Splitting the Data

Next, we will split the dataset into training and testing sets:


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


Training the Lasso Regression Model

Now, we can create an instance of the Lasso regression model and fit it to the training data:


lasso = Lasso(alpha=0.1)
lasso.fit(X_train, y_train)


Making Predictions

Once the model is trained, we can use it to make predictions on the test set:


predictions = lasso.predict(X_test)


Evaluating the Model

Finally, we can evaluate the performance of the Lasso regression model by calculating the mean squared error:


mse = mean_squared_error(y_test, predictions)
print("Mean Squared Error:", mse)


And that’s it! You have successfully performed Lasso regression using Scikit-Learn. With the regularization provided by Lasso, you can prevent overfitting and build more robust models for your machine learning tasks.