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End to End Project - Bikes Assessment - Basic - Train and Analyze the Models - Train Random Forest Model

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 display_scores() function.

INSTRUCTIONS
  • Create a RandomForestRegressor instance, called forest_reg by passing random seed of 42 and n_estimators=150 to the RandomForestRegressor.

  • Call 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)
    

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