figure - residual plot the plot in figure - clearly violates the homoscedasticity assumptionwhich is about residuals being random and not following any pattern to further quantify our findings related to the modelwe plot the cross-validation scores we use the cross_val_score(function available again as part of the model_selection modulewhich is shown in the visualization in figure - figure - cross validation scores the -squared or the coefficient of determination is on an average for -fold cross validation this points to the fact that the predictor is only able to explain of the variance in the target variable you are encouraged to plot and confirm the normality of data it is important to understand if the data can be modeled by linear model or not this is being left as an exercise for you to explore testing the linear regression model prepared and evaluated in the training phase needs to be checked for its performance on completely un-seen datasetthe testing dataset at the beginning of this sectionwe used the train_test_split(function to keep dataset specifically for testing purposes