示例代码

iris鸢尾花数据集是入门级的图像分类数据集，通过这个简单的demo，我们可以快速上手xgboost的使用，具体代码如下：

from sklearn.datasets import load_iris

from xgboost.sklearn import XGBClassifier
from xgboost import plot_importance

import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 记载样本数据集
iris = load_iris()
x,y = iris.data,iris.target

# 数据集分割
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=123457)

clf = XGBClassifier(
    booster = 'gbtree',
    objective = 'multi:softmax',
    num_class = 3,
    gamma = 0.1,
    max_depth = 6,
    reg_lambda = 2,
    subsample = 0.7,
    colsample_bytree = 0.7,
    min_child_weight = 3,
    eta = 0.1,
    seed = 1000,
    nthread = 4,
)

#训练模型
clf.fit(x_train,y_train,eval_metric='auc')

# 对测试集进行预测
y_pred = clf.predict(x_test)
 
#计算准确率
accuracy = accuracy_score(y_test,y_pred)
print('accuracy:%2.2f%%'%(accuracy*100))

# 显示重要特征
plot_importance(clf)
plt.show()

分类结果

最终分类准确率为：

打印特征重要性：