from sklearn.datasets import load_boston from sklearn.linear_model import Ridge from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error # load dataset boston = load_boston() # split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2, random_state=42) # instantiate Ridge regression model ridge = Ridge(alpha=0.5) # train model on training data ridge.fit(X_train, y_train) # make predictions on test data y_pred = ridge.predict(X_test) # evaluate model performance mse = mean_squared_error(y_test, y_pred) print("Mean Squared Error: ", mse)
from sklearn.datasets import load_breast_cancer from sklearn.linear_model import Ridge from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # load dataset cancer = load_breast_cancer() # split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, test_size=0.2, random_state=42) # instantiate Ridge regression model ridge = Ridge(alpha=0.5) # train model on training data ridge.fit(X_train, y_train) # make predictions on test data y_pred = ridge.predict(X_test) # round predictions to nearest integer y_pred = y_pred.round().astype(int) # evaluate model performance acc = accuracy_score(y_test, y_pred) print("Accuracy: ", acc)The package library used in these examples is scikit-learn (sklearn).