This could cause some significant overfitting. Hours, one-hot encoding would have introduced 1440 features instead of 24. The day was represented in minutes since the start of the day instead of However, this introduces a very large number of new features. Treat time progression in a monotonic manner. That the linear regression model benefits from the added flexibility to not The original (ordinal) encoding of the time feature, confirming our intuition The average error rate of this model is 10% which is much better than using More flexibility as we introduce one additional feature per discrete time Using one-hot encoding for the time features gives the linear model a lot Unique values in the “hours” feature), we could decide to treat those asĬategorical variables using a one-hot encoding and thereby ignore anyĪssumption implied by the ordering of the hour values. Since the time features are encoded in a discrete manner using integers (24 Strong negative impact on the predicted number of bike rentals. To 8 should have a strong positive impact on the number of bike rentals whileĪn increase of similar magnitude in the evening from 18 to 20 should have a Linear model from recognizing that an increase of hour in the morning from 6 Non-linear terms have to be engineered inįor example, the raw numerical encoding of the "hour" feature prevents the Regression does not automatically model non-monotonic relationships between The linear regression model to properly leverage the time information: linear (merely min-max scaled) of the periodic time-related features might prevent We can suspect that the naive original encoding This is more than three times higher than the average error of the The performance is not good: the average error is around 14% of the maximumĭemand. We only try the default hyper-parameters for this model: The numerical variables need no preprocessing and, for the sake of simplicity, This also has the added benefit of preventing any issue with unknown Pass the list of categorical values explicitly to use a logical order whenĮncoding the categories as integers instead of the lexicographical order. Let the model know that it should treat those as categorical variables by Here, we do minimal ordinal encoding for the categorical variables and then Numerical features as long as the number of samples is large enough. Gradient Boosting Regression with decision trees is often flexible enough toĮfficiently handle heteorogenous tabular data with a mix of categorical and We are now ready to do some predictive modeling! Gradient Boosting ¶
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