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Project - Bike Rental Forecasting - Basic

End to End Project - Bikes Assessment - Basic - Train and Analyze the Models - Train Linear Regression Model

Train the Linear Regression 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 LinearRegression instance, called lin_reg, using default constructor (without any arguments)

  • 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:

    LinearRegression object lin_reg
    trainingCols dataframe
    trainingLabels dataframe
    parameter cv with value 10 (cv=10)
    scoring parameter with value "neg_mean_absolute_error"
    lr_mae_scores = -cross_val_score(<<your code comes here>>)
    display_scores(lr_mae_scores)
    
  • Call cross_val_score() function, to perform training and cross validation and to calculate the mean squared error scores, by passing to it the following:

    LinearRegression object lin_reg
    trainingCols dataframe
    trainingLabels dataframe
    parameter cv with value 10 (cv=10)
    scoring parameter with value "neg_mean_squared_error"
    lr_mse_scores = np.sqrt(-cross_val_score(<<your code comes here>>))
    display_scores(lr_mse_scores)
    

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