from numpy import loadtxt

from xgboost import XGBClassifier

from xgboost import plot_importance

from matplotlib import pyplot

# load data

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

# split data into X and y

X = dataset[:,0:8]

y = dataset[:,8]

# fit model no training data

model = XGBClassifier()

model.fit(X, y)

# plot feature importance

plot_importance(model)

pyplot.show()

from numpy import loadtxt

from xgboost import XGBClassifier

from sklearn.model_selection import GridSearchCV

from sklearn.model_selection import StratifiedKFold

# load data

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

# split data into X and y

X = dataset[:,0:8]

Y = dataset[:,8]

# grid search

model = XGBClassifier()

learning_rate = [0.0001, 0.001, 0.01, 0.1, 0.2, 0.3]

param_grid = dict(learning_rate=learning_rate)

kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=7)

grid_search = GridSearchCV(model, param_grid, scoring="neg_log_loss", n_jobs=-1, cv=kfold)

grid_result = grid_search.fit(X, Y)

# summarize results

print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))

means = grid_result.cv_results_['mean_test_score']

params = grid_result.cv_results_['params']

for mean, param in zip(means, params):

    print("%f  with: %r" % (mean, param))

xgb1 = XGBClassifier(

 learning_rate =0.1,

 n_estimators=1000,

 max_depth=5,

 min_child_weight=1,

 gamma=0,

 subsample=0.8,

 colsample_bytree=0.8,

 objective= 'binary:logistic',

 nthread=4,

 scale_pos_weight=1,

 seed=27)

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()

eval_set = [(X_test, y_test)]

model.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="logloss", eval_set=eval_set, verbose=True)

# 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))

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()

eval_set = [(X_test, y_test)]

model.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="logloss", eval_set=eval_set, verbose=True)

# 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))