makes this seem like good model to start with we use the posix time feature and pass random forest regressor to our eval_on_features function figure - shows the resultin[ ]from sklearn ensemble import randomforestregressor regressor randomforestregressor(n_estimators= random_state= plt figure(eval_on_features(xyregressorout[ ]test-set ^ - figure - predictions made by random forest using only the posix time the predictions on the training set are quite goodas is usual for random forests howeverfor the test seta constant line is predicted the is - which means that we learned nothing what happenedthe problem lies in the combination of our feature and the random forest the value of the posix time feature for the test set is outside of the range of the feature values in the training setthe points in the test set have timestamps that are later than all the points in the training set treesand therefore random forestscannot extrapolate to feature ranges outside the training set the result is that the model simply predicts the target value of the closest point in the training set--which is the last time it observed any data clearly we can do better than this this is where our "expert knowledgecomes in from looking at the rental figures in the training datatwo factors seem to be very importantthe time of day and the day of the week solet' add these two features we can' really learn anything from the posix timeso we drop that feature firstlet' use only the hour of the day as figure - showsnow the predictions have the same pattern for each day of the weekutilizing expert knowledge