[ML] XGBClassifier sample code

# First XGBoost model for Pima Indians dataset

from numpy import loadtxt

from xgboost import XGBClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

# load data

dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",")

# split data into X and y

X = dataset[:,0:8]

Y = dataset[:,8]

# split data into train and test sets

seed = 7

test_size = 0.33

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)

# fit model no training data

model = XGBClassifier()

model.fit(X_train, y_train)

# make predictions for test data

y_pred = model.predict(X_test)

predictions = [round(value) for value in y_pred]

# evaluate predictions

accuracy = accuracy_score(y_test, predictions)

print("Accuracy: %.2f%%" % (accuracy * 100.0))