نوشته آزمایشی

				
					def linear_assumption(model, features, label):
    """
    Linearity: Assumes that there is a linear relationship between the predictors and
               the response variable. If not, either a quadratic term or another
               algorithm should be used.
    """
    print('Assumption 1: Linear Relationship between the Target and the Feature', '\n')
        
    print('Checking with a scatter plot of actual vs. predicted.',
           'Predictions should follow the diagonal line.')
    
    # Calculating residuals for the plot
    df_results = calculate_residuals(model, features, label)
    
    # Plotting the actual vs predicted values
    sns.lmplot(x='Actual', y='Predicted', data=df_results, fit_reg=False, size=7)
        
    # Plotting the diagonal line
    line_coords = np.arange(df_results.min().min(), df_results.max().max())
    plt.plot(line_coords, line_coords,  # X and y points
             color='darkorange', linestyle='--')
    plt.title('Actual vs. Predicted')
    plt.show()