def rmse(y, y_hat):
diff = np.power(y - y_hat, 2)
return np.sqrt(np.sum(diff))
# load test data
boston = load_boston()
n = 10000
# create data matrix
data = arboretum.DMatrix(boston.data[0:n], y=boston.target[0:n])
y = boston.target[0:n]
# init model
model = arboretum.Garden('reg:linear', data, 6, 2, 1, 0.5)
# grow trees
for i in xrange(5):
model.grow_tree()
# predict on train data set
pred = model.predict(data)
# print first n records
print pred[0:10]
#RMSE
print rmse(pred, y)
'gpu': True
},
'tree': {
'eta': 0.2,
'max_depth': 6,
'gamma': 0.0,
'min_child_weight': 2.0,
'min_leaf_size': 0,
'colsample_bytree': 1.0,
'colsample_bylevel': 1.0,
'lambda': 0.0,
'alpha': 0.0
}
})
model = arboretum.Garden(config, data)
# grow trees
for i in range(2):
model.grow_tree()
# predict on train data set
pred = model.predict(data)
# print first n records
print(pred)
mat = xgboost.DMatrix(iris.data[index, 0:n], label=y)
param = {'max_depth': 5, 'silent': True, 'objective': "reg:logistic"}
param['nthread'] = 1
param['min_child_weight'] = 2
