XGBoost_HPTuning.py

import xgboost as xgb
from sklearn.model_selection import RandomizedSearchCV

params = {'max_depth': [3, 6, 8,10,12],
          'learning_rate': [0.01, 0.1, 0.2, 0.001, 0.3],
          'colsample_bytree': np.arange(0.3, 1.0, 0.1),
          'colsample_bylevel': np.arange(0.3, 1.0, 0.1),
          'subsample': np.arange(0.3, 1.0, 0.1),
          'n_estimators': [100, 150, 200, 250,300]
          }

xgbclf = xgb.XGBClassifier(objective="multi:softmax", tree_method='hist')
clf = RandomizedSearchCV(estimator=xgbclf,
                         param_distributions=params,
                         scoring='accuracy',
                         n_iter=20,
                         n_jobs=4,
                         verbose=2)

clf.fit(x_train, y_train)
best_combination = clf.best_params_