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from sklearn.model_selection import train_test_split | |
from xboost import XGBClassifier | |
X = #yours data | |
y = #labels | |
train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.3, random_state=100) | |
parameters = {'nthread':[6], | |
'objective':['binary:logistic'], | |
'learning_rate': [0.01, 0.1], | |
'max_depth': [5,8,13], | |
'n_estimators': [200,500,1000,3000], | |
'seed': [1337]} | |
clf = XGBClassifier(param_grid=parameters) | |
clf.fit(X_train, y_train) | |
predictions = clf.predict(test_X) |
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