机器学习之决策树（下）

在决策树中有一个很重要的概念就是深度

没错决策树很容易过拟合

从iris来看下所谓的过拟合

环境

• jupyter notebook

导入包

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_iris
import pydotplus
mpl.rcParams['font.sans-serif'] = ['simHei']
mpl.rcParams['axes.unicode_minus'] = False

iris_feature_E = 'sepal length', 'sepal width', 'petal length', 'petal width'
iris_feature = '花萼长度', '花萼宽度', '花瓣长度', '花瓣宽度'
iris_class = 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'
# 加载数据
x = pd.DataFrame(load_iris().data)
y = load_iris().target

图片是二维的，所以只能使用两个特征

# 为了可视化，仅使用前两列特征
x = x[[0,1]]
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)
model = DecisionTreeClassifier