Python Coding Random forest classifier using xgBoost














































Python Coding Random forest classifier using xgBoost



Python Coding Random forest classifier using xgBoost

import xgboost as xgb
from xgboost import XGBClassifier
params = {"loss":"deviance",
"max_depth":10,
"n_estimators":100}
xgb_clf = XGBClassifier(**params)
xgb_clf.fit(train_data, train_label)
params2 = {'base_score': 0.5,
'booster': 'gbtree',
'colsample_bylevel': 1,
'colsample_bytree': 1,
'gamma': 0,
'learning_rate': 0.1,
'max_delta_step': 0,
'max_depth': 3,
'min_child_weight': 1,
'missing': None,
'n_estimators': 100,
'n_jobs': 1,
'nthread': None,
'objective': 'multi:softprob',
'random_state': 0,
'reg_alpha': 0,
'reg_lambda': 1,
'scale_pos_weight': 1,
'seed': None,
'silent': True,
'subsample': 1}
xgb_clf.get_params()
y_pred_xgb = xgb_clf.predict(test_data)
y_pred_xgb[:5]
correct = np.nonzero(test_label == y_pred_xgb)[0]
wrong = np.nonzero(test_label != y_pred_xgb)[0]
print(classification_report(test_label, y_pred_xgb, target_names=label_names))
print(accuracy_score(test_label, y_pred_xgb)) 
 Now after applying XGBoost, we are getting an accuracy of 90%.

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