数据集地址
- 分类：http://archive.ics.uci.edu/ml/datasets/Iris 部分数据：
基于sklearn接口的分类
代码语言：javascript复制from pprint import pprint
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.externals import joblib
import numpy as np
from xgboost.sklearn import XGBClassifier
# 以分隔符,读取文件，得到的是一个二维列表
iris = np.loadtxt('iris.data', dtype=str, delimiter=',', unpack=False, encoding='utf-8')
# 前4列是特征
data = iris[:, :4].astype(np.float)
# 最后一列是标签，我们将其转换为二维列表
target = iris[:, -1][:, np.newaxis]
# 对标签进行onehot编码后还原成数字
enc = OneHotEncoder()
target = enc.fit_transform(target).astype(np.int).toarray()
target = [list(oh).index(1) for oh in target]
# 划分训练数据和测试数据
X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=1)
# 模型训练
params = {
'n_estimators': 100,
'max_depth': 5,
'min_child_weight': 1,
'subsample': 0.8,
'colsample_bytree': 0.8,
'reg_alpha': 0,
'reg_lambda': 1,
'learning_rate': 0.1}
xgb = XGBClassifier(random_state=1, **params)
xgb.fit(X_train, y_train)
# 模型存储
joblib.dump(xgb, 'xgb_model.pkl')
# 模型加载
gbdt = joblib.load('xgb_model.pkl')
# 模型预测
y_pred = xgb.predict(X_test)
# 模型评估
print('The accuracy of prediction is:', accuracy_score(y_test, y_pred))
# 特征重要度
print('Feature importances:', list(xgb.feature_importances_))结果
The accuracy of prediction is: 0.9666666666666667 Feature importances: [0.002148238569679191, 0.0046703830672789074, 0.33366676380518245, 0.6595146145578594]
基于sklearn接口的回归
代码语言：javascript复制from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from xgboost.sklearn import XGBRegressor
from sklearn.metrics import mean_absolute_error
X, y = make_regression(n_samples=100, n_features=1, noise=20)
# 切分训练集、测试集
train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.25, random_state=1)
# 调用XGBoost模型，使用训练集数据进行训练（拟合）
my_model = XGBRegressor(
max_depth=30,
learning_rate=0.01,
n_estimators=5,
silent=True,
objective='reg:linear',
booster='gblinear',
n_jobs=50,
nthread=None,
gamma=0,
min_child_weight=1,
max_delta_step=0,
subsample=1,
colsample_bytree=1,
colsample_bylevel=1,
reg_alpha=0,
reg_lambda=1,
scale_pos_weight=1,
base_score=0.5,
random_state=0,
seed=None,
missing=None,
importance_type='gain')
my_model.fit(train_X, train_y)
# 使用模型对测试集数据进行预测
predictions = my_model.predict(test_X)
# 对模型的预测结果进行评判（平均绝对误差）
print("Mean Absolute Error : " str(mean_absolute_error(predictions, test_y)))结果：
Mean Absolute Error : 47.98486383348952
