explain of l1 regularization

Author: devshah21

ml-concepts/l1_regularization.py

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+from sklearn.linear_model import Lasso
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import fetch_california_housing
+from sklearn.metrics import mean_squared_error
+
+data = fetch_california_housing()
+X = data.data
+y = data.target
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+# define the L1 / lasso regression model with a chosen alpha (λ)
+lasso = Lasso(alpha=0.1)
+
+lasso.fit(X_train, y_train)
+
+y_pred = lasso.predict(X_test)
+
+# eval the model
+mse = mean_squared_error(y_test, y_pred)
+print(f'Mean Squared Error: {mse}')
+
+print('Coefficients:', lasso.coef_)