"reg_alpha": 0.15,
"reg_lambda": 0.15,
'n_gpus': 0
}
classifier = XGBClassifier(objective='multiclass',
num_class=4,
metric="multi_error",
learning_rate=0.1,
min_child_weight=40,
feature_fraction=0.8,
reg_alpha=0.15,
reg_lambda=0.15)
n_classifier = len(classifier)
# Xgboost Classifier and print accuracy score
for index, (name, classifier) in enumerate(classifier.items()):
classifier.fit(X_Train, Y_Train)
Y_pred = classifier.predict(X_Test)
Y_pred = pd.DataFrame(Y_pred)
Y_pred.describe()
accuracy = accuracy_score(Y_Test, Y_pred)
print("Accuracy (Train) for %s: %0.1f%% " % (name, accuracy * 100))
# get probabilities
probas = classifier.predict_proba(X_Test)
probas
# Confusion Matrix
cm = confusion_matrix(Y_Test, Y_pred)
print(cm)
