ML | Implementing L1 and L2 regularization using Sklearn

Prerequisites: L2 and L1 regularization

This article aims to implement the L2 and L1 regularization for Linear regression using the Ridge and Lasso modules of the Sklearn library of Python.
Dataset – House prices dataset .

Step 1: Importing the required libraries

filter_none

edit
close

play_arrow

link
brightness_4
code

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression, Ridge, Lasso
from sklearn.model_selection import train_test_split, cross_val_score
from statistics import mean

chevron_right


Step 2: Loading and cleaning the Data

filter_none

edit
close

play_arrow

link
brightness_4
code

# Changing the working location to the location of the data
cd C:\Users\Dev\Desktop\Kaggle\House Prices
  
# Loading the data into a Pandas DataFrame
data = pd.read_csv('kc_house_data.csv')
  
# Dropping the numerically non-sensical variables
dropColumns = ['id', 'date', 'zipcode']
data = data.drop(dropColumns, axis = 1)
  
# Seperating the dependent and independent variables
y = data['price']
X = data.drop('price', axis = 1)
  
# Dividing the data into training and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)

chevron_right


Step 3: Building and evaluating the different models

a) Linear Regression:

filter_none

edit
close

play_arrow

link
brightness_4
code

# Bulding and fitting the Linear Regression model
linearModel = LinearRegression()
linearModel.fit(X_train, y_train)
  
# Evaluating the Linear Regression model
print(linearModel.score(X_test, y_test))

chevron_right


b) Ridge(L2) Regression:

filter_none

edit
close

play_arrow

link
brightness_4
code

# List to maintain the different cross-validation scores
cross_val_scores_ridge = []
  
# List to maintain the different values of alpha
alpha = []
  
# Loop to compute the different values of cross-validation scores
for i in range(1, 9):
    ridgeModel = Ridge(alpha = i * 0.25)
    ridgeModel.fit(X_train, y_train)
    scores = cross_val_score(ridgeModel, X, y, cv = 10)
    avg_cross_val_score = mean(scores)*100
    cross_val_scores_ridge.append(avg_cross_val_score)
    alpha.append(i * 0.25)
  
# Loop to print the different values of cross-validation scores
for i in range(0, len(alpha)):
    print(str(alpha[i])+' : '+str(cross_val_scores_ridge[i]))

chevron_right


From the above output, we can conclude that the best value of alpha for the data is 2.

filter_none

edit
close

play_arrow

link
brightness_4
code

# Building and fitting the Ridge Regression model
ridgeModelChosen = Ridge(alpha = 2)
ridgeModelChosen.fit(X_train, y_train)
  
# Evaluating the Ridge Regression model
print(ridgeModelChosen.score(X_test, y_test))

chevron_right


c) Lasso(L1) Regression:

filter_none

edit
close

play_arrow

link
brightness_4
code

# List to maintain the cross-validation scores
cross_val_scores_lasso = []
  
# List to maintain the different values of Lambda
Lambda = []
  
# Loop to compute the cross-validation scores
for i in range(1, 9):
    lassoModel = Lasso(alpha = i * 0.25, tol = 0.0925)
    lassoModel.fit(X_train, y_train)
    scores = cross_val_score(lassoModel, X, y, cv = 10)
    avg_cross_val_score = mean(scores)*100
    cross_val_scores_lasso.append(avg_cross_val_score)
    Lambda.append(i * 0.25)
  
# Loop to print the different values of cross-validation scores
for i in range(0, len(alpha)):
    print(str(alpha[i])+' : '+str(cross_val_scores_lasso[i]))

chevron_right


From the above output, we can conclude that the best value of lambda is 2.

filter_none

edit
close

play_arrow

link
brightness_4
code

# Building and fitting the Lasso Regression Model
lassoModelChosen = Lasso(alpha = 2, tol = 0.0925)
lassoModelChosen.fit(X_train, y_train)
  
# Evaluating the Lasso Regression model
print(lassoModelChosen.score(X_test, y_test))

chevron_right


Step 4: Comparing and Visualizing the results

filter_none

edit
close

play_arrow

link
brightness_4
code

# Building the two lists for visualization
models = ['Linear Regression', 'Ridge Regression', 'Lasso Regression']
scores = [linearModel.score(X_test, y_test),
         ridgeModelChosen.score(X_test, y_test),
         lassoModelChosen.score(X_test, y_test)]
  
# Building the dictionary to compare the scores
mapping = {}
mapping['Linear Regreesion'] = linearModel.score(X_test, y_test)
mapping['Ridge Regreesion'] = ridgeModelChosen.score(X_test, y_test)
mapping['Lasso Regression'] = lassoModelChosen.score(X_test, y_test)
  
# Printing the scores for different models
for key, val in mapping.items():
    print(str(key)+' : '+str(val))

chevron_right


filter_none

edit
close

play_arrow

link
brightness_4
code

# Plotting the scores
plt.bar(models, scores)
plt.xlabel('Regression Models')
plt.ylabel('Score')
plt.show()

chevron_right




My Personal Notes arrow_drop_up

Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.




Article Tags :
Practice Tags :


Be the First to upvote.


Please write to us at contribute@geeksforgeeks.org to report any issue with the above content.