Visualize the xgboost bike rental model

You've now seen three different ways to model the bike rental data. For this example, you've seen that the gradient boosting model had the smallest RMSE. To finish up the course, let's compare the gradient boosting model's predictions to the other two models as a function of time.

On completing this exercise, you will have completed the course. Congratulations! Now you have the tools to apply a variety of approaches to your regression tasks.

The data frame bikesAugust with predictions,has been pre-loaded. The plots quasipoisson_plot and randomforest_plot are also available.

Print quasipoisson_plot to review the quasipoisson model's behavior.
Print randomforest_plot to review the random forest model's behavior.
Fill in the blanks to plot the gradient boosting predictions and actual counts by hour for the first 14 days of August.
- gather() the cnt and pred columns into a column called value, with a key called valuetype.
- Plot value as a function of instant (day).

How does the gradient boosting model compare to the previous models?

Exercise

Visualize the xgboost bike rental model

Instructions