In machine learning we often use linear regression to make predictions based on data. But sometimes the model learns the training data too well including the noise or errors. This is called overfitting and it makes the model perform badly on new data.<\/p>\n\n\n\n
To solve this problem we use a method called regularization. It helps control how much the model depends on each feature. Two common types of regularization are called Ridge and Lasso Regression.<\/p>\n\n\n\n
Ridge and Lasso Regression are better versions of linear regression<\/a>. They add a penalty to the model to keep it simple and prevent overfitting. Ridge Regression makes all feature weights smaller but keeps them in the model. Lasso Regression can remove the unimportant ones by making their weights zero. In this blog you will learn about ridge and lasso regression in Python and see how to use them with examples.<\/p>\n\n\n
![\"ridge](\"https:\/\/www.iquanta.in\/blog\/wp-content\/uploads\/2025\/06\/image-2.png\")
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