“Python Cross Validierung” Code-Antworten

Python Cross Validierung

from sklearn.model_selection import cross_val_score
scores = cross_val_score(model   # Ridge(alpha=1)
                       , X_train # scaler.fit_transform(x_train)
                       , y_train # scaler.fit_transform(y_train)
                       , scoring='neg_mean_squared_error' # depends on model more at https://scikit-learn.org/stable/modules/model_evaluation.html
                       , cv=5)   # 5 fold cross validation
mean(scores) # negMSE (higher=better), adj hyper params to optimize 
model.fit(x_train, y_train)      # make sure to fit the model again after 
y_final_test_pred = model.predict(x_test) # Final predictions
mean_squared_error(y_test, y_final_test_pred) # Final MSE on 'new' data