Train XGBoost models
Every Machine Learning method could potentially overfit. You will see it on this example with XGBoost. Again, you are working with the Store Item Demand Forecasting Challenge. The train DataFrame is available in your workspace.
Firstly, let's train multiple XGBoost models with different sets of hyperparameters using XGBoost's learning API. The single hyperparameter you will change is:
max_depth- maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit.
Diese Übung ist Teil des Kurses
Winning a Kaggle Competition in Python
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
import xgboost as xgb
# Create DMatrix on train data
dtrain = xgb.DMatrix(data=train[['store', 'item']],
label=train['sales'])
# Define xgboost parameters
params = {'objective': 'reg:linear',
'____': ____,
'verbosity': 0}
# Train xgboost model
xg_depth_2 = xgb.train(params=params, dtrain=dtrain)