I am trying to perform regression using XGBoost. My dataset has all positive values but some of the predictions are negative.
I read on this link that reducing the number of trees might help the situation.
I reduced the estimators from 700 to 570 and the number of negative predictions decreased but is there any way to remove these negative predictions? When I further tried reducing the estimators to 400, I got worse results with high rmse and more negative predictions.
I looked at the negative predictions to understand where the model went wrong(see below image):
It looks like if I take the absolute value of the negative predictions then that would be sufficient but is this the right method?
My code is:
!pip install xgboost !pip install scikit-optimize from xgboost import XGBRegressor final_XGB=XGBRegressor(random_state=123,gamma= 24.47 ,learning_rate=0.1235,max_depth=10,min_child_weight=0.21509999999999999, n_estimators=570,subsample=0.74,reg_lambda=0.8) from sklearn.model_selection import cross_validate cross_val_scores=cross_validate(final_XGB,X_train,y_train,cv=3,scoring=['neg_mean_squared_error','r2'],verbose=1,return_train_score=True,n_jobs=-1 ) cross_val_scores['test_r2'].mean() #returns 0.9595609470775659
A bit more about the dataset. I am trying to predict the number of people that would be present at a given location at a given time period.
In order to predict the count of people, I have taken the
- hour of day,
- peak(is it a public holiday?),
- Area (area of location in m2)
- location name.
The location names were string values (eg: Location1, Location2, etc.) so I transformed them using JamesSteinEncoder.
There are no missing values and I have removed a few outliers from the dataset.
Regarding the relationship between "Actual" and "Error" column, I have plotted the graph below:
Snapshot of final results:
xgboostaccommodates that with
objective='count:poisson'. Cryo's logarithmic transform is also worth trying; if you have zeros in your target, transform instead as $\log(1+Y)$ or something similar, rather than skipping the transformation on zeros as in your comment. Note that when log-transforming, your error optimization will inherently be multiplicative: for a true value of 10, predicting 100 or 1 have the same "wrongness". $\endgroup$