from sklearn.dummy import DummyRegressor from sklearn.datasets import make_regression # Create random regression dataset X, y = make_regression(n_samples=100, n_features=10, random_state=42) # Fit DummyRegressor model dummy = DummyRegressor(strategy='mean') dummy.fit(X, y) # Predict target values for new data y_pred = dummy.predict(X)
import numpy as np from sklearn.linear_model import LinearRegression from sklearn.model_selection import cross_val_score # Create random regression dataset X, y = make_regression(n_samples=100, n_features=10, random_state=42) # Fit DummyRegressor and LinearRegression models dummy = DummyRegressor(strategy='mean') lr = LinearRegression() dummy_score = np.mean(cross_val_score(dummy, X, y, cv=5, scoring='neg_mean_squared_error')) lr_score = np.mean(cross_val_score(lr, X, y, cv=5, scoring='neg_mean_squared_error')) # Compare mean squared error scores print("DummyRegressor MSE: {:.2f}".format(-dummy_score)) print("LinearRegression MSE: {:.2f}".format(-lr_score))In both examples, the sklearn.dummy package library is used to import the DummyRegressor model. Additional packages used include scikit-learn datasets, numpy, and scikit-learn model selection.