目标识别笔记：鸢尾花数据集GBDT分类的python仿真

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import load_boston
from sklearn.datasets import load_iris
from sklearn.tree import plot_tree

iris = load_iris()  #加载鸢尾花数据集
data = iris.data  #特征数据
target = iris.target  #分类数据

params = {'n_estimators': 2, # 弱分类器的个数
          'max_depth': 2,       # 弱分类器（CART回归树）的最大深度
          'learning_rate': 0.1 }           

GBDTreg = GradientBoostingClassifier(**params)
GBDTreg.fit(data, target)
for ii in range(0,GBDTreg.n_estimators):
    for jj in range(0,3):
        sub_tree = GBDTreg.estimators_[ii,jj] # GBDTreg.estimators_.shape = (2,3)，三分类
        plt.figure(figsize = (15,9))
        plot_tree(sub_tree)
y_predict = GBDTreg.predict(data)

结果如下：