eval_on_features(x_hour_week_onehotyridge()out[ ]test-set ^ figure - predictions made by linear regression using one-hot encoding of hour of day and day of week this gives us much better match than the continuous feature encoding now the linear model learns one coefficient for each day of the weekand one coefficient for each time of the day that means that the "time of daypattern is shared over all days of the weekthough using interaction featureswe can allow the model to learn one coefficient for each combination of day and time of day (see figure - )in[ ]poly_transformer polynomialfeatures(degree= interaction_only=trueinclude_bias=falsex_hour_week_onehot_poly poly_transformer fit_transform(x_hour_week_onehotlr ridge(eval_on_features(x_hour_week_onehot_polyylrout[ ]test-set ^ representing data and engineering features