Halloween Sale: Flat 70% + Addl. 25% Off + 30 Days Extra Lab on all Courses | Use Coupon HS25 in Checkout | Offer Expires InEnroll Now
Train the Random Forest Model on the 'Training' data set using cross validation and calculate 'mean absolute error' and 'root mean squared error' (RMSE) for this model.
Display these scores using
Create a RandomForestRegressor instance, called
forest_reg by passing random seed of 42 and
n_estimators=150 to the RandomForestRegressor.
cross_val_score() function, to perform training and cross validation and to calculate the mean absolute error scores, by passing to it the following:
RandomForestRegressor object forest_reg trainingCols dataframe trainingLabels dataframe parameter cv with value 10 (cv=10) scoring parameter with value "neg_mean_absolute_error" rf_mae_scores = -cross_val_score(<<your code comes here>>) display_scores(rf_mae_scores)
No hints are availble for this assesment
Answer is not availble for this assesment
Note - Having trouble with the assessment engine? Follow the steps listed here