外贸网站建设哪里好,如何帮人做网站赚钱吗,wordpress配置资源,网站公司技术交接#x1f368; 本文为#x1f517;365天深度学习训练营 中的学习记录博客#x1f356; 原作者#xff1a;K同学啊 前言 机器学习是深度学习和数据分析的基础#xff0c;接下来将更新常见的机器学习算法注意#xff1a;在打数学建模比赛中#xff0c;机器学习用的也很多 本文为365天深度学习训练营 中的学习记录博客 原作者K同学啊 前言 机器学习是深度学习和数据分析的基础接下来将更新常见的机器学习算法注意在打数学建模比赛中机器学习用的也很多可以一起学习 决策树模型数学原理很复杂强烈推荐看书看书看书这里推荐《统计学习方法》和《机器学习西瓜书》。这里只是介绍了决策树组成但是原理没有详细介绍后面会出详介绍篇章。 最近开学更新不太及时请大家见谅欢迎收藏 点赞 关注 文章目录 决策树模型简介建立决策树的方法 分类案例导入数据和数据分析划分自变量和因变量模型训练模型预测结果 回归案例导入数据划分数据创建模型模型预测与训练模型评估树图绘制 决策树模型
简介
定义(统计学习方法)分类决策树模型是一种描述对实例进行分类的树形结构决策树由节点、有向边组成节点类型有两种内部节点和叶子节点内部节点表示一个特征或者属性叶子节点表示一个类。
决策树与if-then
学过任何语言的人都知道if-else结构决策树也是这样如果if满足某一种条件则归到一类不满足条件的归到另外一类如此循环判断一直到所有特征、属性和类都归类到某一类最终形成一颗树注意一个原则互斥且完备。
决策树过程
特征选择、建立决策树、决策树剪枝三个过程
决策树解决问题
回归和分类如果分类的叶子节点就是回归否则就是分类。
建立决策树的方法 决策树背后由很多的数学原理这里只介绍信息增益、信息增益比、基尼系数其他的概念推荐翻阅统计学习方法和西瓜书想要电子版资料的可以私聊我。
建议这一部分一定要看书推荐统计学习方法和机器学习西瓜书书中有很详细的案例帮助我们理解。67y
以下概念均来自于《统计学习方法》
信息增益 特征A对训练数据集D的信息增益g(D.A)定义为集合D的经验熵H(D)与特征A给定条件下D的经验条件H(DA)之差即
g ( D , A ) H ( D ) − H ( D ∣ A ) g\left(D,A\right)H\left(D\right)-H\left(D|A\right) g(D,A)H(D)−H(D∣A)
信息增益比特征A对训练数据集D的信息增益比gR(D)定为其信息增益 g(D,A)与训练数据集 D 关于特征 A的值的熵 HA(D)之比。即
g R ( D , A ) g ( D , A ) H A ( D ) g_{R}(D,A)\frac{g(D,A)}{H_{A}(D)} gR(D,A)HA(D)g(D,A)
其中 H A ( D ) − ∑ i 1 n ∣ D i ∣ ∣ D ∣ log 2 ∣ D i ∣ ∣ D ∣ H_{A}(D)-\sum_{i1}^{n}\frac{\left|D_{i}\right|}{\left|D\right|}\log_{2}\frac{\left|D_{i}\right|}{\left|D\right|} HA(D)−i1∑n∣D∣∣Di∣log2∣D∣∣Di∣ n表示特征A的数量。
基尼指数分类问题中假设有区个类样本点属于第k 类的概率为 pk则概率分布的基尼指数定义为
G i n i ( p ) ∑ k 1 K p k ( 1 − p k ) 1 − ∑ k 1 K p k 2 Gini\left(p\right)\sum_{k1}^{K}p_{k}\left(1-p_{k}\right)1-\sum_{k1}^{K}p_{k}^{2} Gini(p)k1∑Kpk(1−pk)1−k1∑Kpk2
建议看书通过案例和公式来理解。
分类案例
简介通过鸢尾花的叶子特征构建判别叶子类别的树。
导入数据和数据分析
import numpy as np
import pandas as pd url https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
columns [花萼-length, 花萼-width, 花瓣-length, 花瓣-width, class]data pd.read_csv(url, namescolumns)
data花萼-length花萼-width花瓣-length花瓣-widthclass05.13.51.40.2Iris-setosa14.93.01.40.2Iris-setosa24.73.21.30.2Iris-setosa34.63.11.50.2Iris-setosa45.03.61.40.2Iris-setosa..................1456.73.05.22.3Iris-virginica1466.32.55.01.9Iris-virginica1476.53.05.22.0Iris-virginica1486.23.45.42.3Iris-virginica1495.93.05.11.8Iris-virginica
150 rows × 5 columns
# 查看值的类别和数量
data[class].value_counts()结果
class
Iris-setosa 50
Iris-versicolor 50
Iris-virginica 50
Name: count, dtype: int64# 查看变量信息
data.info()class pandas.core.frame.DataFrame
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):# Column Non-Null Count Dtype
--- ------ -------------- ----- 0 花萼-length 150 non-null float641 花萼-width 150 non-null float642 花瓣-length 150 non-null float643 花瓣-width 150 non-null float644 class 150 non-null object
dtypes: float64(4), object(1)
memory usage: 6.0 KB# 查看缺失值
data.isnull().sum()结果
花萼-length 0
花萼-width 0
花瓣-length 0
花瓣-width 0
class 0
dtype: int64# 查看特征的统计变量
data.describe()结果
花萼-length花萼-width花瓣-length花瓣-widthcount150.000000150.000000150.000000150.000000mean5.8433333.0540003.7586671.198667std0.8280660.4335941.7644200.763161min4.3000002.0000001.0000000.10000025%5.1000002.8000001.6000000.30000050%5.8000003.0000004.3500001.30000075%6.4000003.3000005.1000001.800000max7.9000004.4000006.9000002.500000
# 查看特征变量的相关性
name_corr [花萼-length, 花萼-width, 花瓣-length, 花瓣-width]
corr data[name_corr].corr()
print(corr)花萼-length 花萼-width 花瓣-length 花瓣-width
花萼-length 1.000000 -0.109369 0.871754 0.817954
花萼-width -0.109369 1.000000 -0.420516 -0.356544
花瓣-length 0.871754 -0.420516 1.000000 0.962757
花瓣-width 0.817954 -0.356544 0.962757 1.000000说明特征变量之间存在共线性问题
划分自变量和因变量
X data.iloc[:, [0, 1, 2, 3]].values # .values转化成矩阵
y data.iloc[:, 4].values模型训练
from sklearn import treemodel tree.DecisionTreeClassifier()
model.fit(X, y) # 模型训练# 打印模型结构
r tree.export_text(model)模型预测结果
# 随机选取值
x_test X[[0, 30, 60, 90, 120, 130], :]
y_pred_prob model.predict_proba(x_test) # 预测概率
y_pred model.predict(x_test) # 预测值print(\n模型)
print(r)模型
|--- feature_3 0.80
| |--- class: Iris-setosa
|--- feature_3 0.80
| |--- feature_3 1.75
| | |--- feature_2 4.95
| | | |--- feature_3 1.65
| | | | |--- class: Iris-versicolor
| | | |--- feature_3 1.65
| | | | |--- class: Iris-virginica
| | |--- feature_2 4.95
| | | |--- feature_3 1.55
| | | | |--- class: Iris-virginica
| | | |--- feature_3 1.55
| | | | |--- feature_2 5.45
| | | | | |--- class: Iris-versicolor
| | | | |--- feature_2 5.45
| | | | | |--- class: Iris-virginica
| |--- feature_3 1.75
| | |--- feature_2 4.85
| | | |--- feature_0 5.95
| | | | |--- class: Iris-versicolor
| | | |--- feature_0 5.95
| | | | |--- class: Iris-virginica
| | |--- feature_2 4.85
| | | |--- class: Iris-virginicaprint(\n测试数据)
print(x_test)测试数据
[[5.1 3.5 1.4 0.2][4.8 3.1 1.6 0.2][5. 2. 3.5 1. ][5.5 2.6 4.4 1.2][6.9 3.2 5.7 2.3][7.4 2.8 6.1 1.9]]print(\n预测所属类别概率)
print(y_pred_prob)预测所属类别概率
[[1. 0. 0.][1. 0. 0.][0. 1. 0.][0. 1. 0.][0. 0. 1.][0. 0. 1.]]print(\n测试所属类别)
print(y_pred)测试所属类别
[Iris-setosa Iris-setosa Iris-versicolor Iris-versicolorIris-virginica Iris-virginica]回归案例
通过鸢尾花三个特征预测花瓣长度。
导入数据
import pandas as pd
import numpy as np url https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
names [花萼-width, 花萼-length, 花瓣-width, 花瓣-length, class]data pd.read_csv(url, namesnames)
data花萼-width花萼-length花瓣-width花瓣-lengthclass05.13.51.40.2Iris-setosa14.93.01.40.2Iris-setosa24.73.21.30.2Iris-setosa34.63.11.50.2Iris-setosa45.03.61.40.2Iris-setosa..................1456.73.05.22.3Iris-virginica1466.32.55.01.9Iris-virginica1476.53.05.22.0Iris-virginica1486.23.45.42.3Iris-virginica1495.93.05.11.8Iris-virginica
150 rows × 5 columns
划分数据
# 划分数据
X data.iloc[:, [0, 1, 2]]
y data.iloc[:, 3]创建模型
from sklearn import tree model tree.DecisionTreeRegressor()
model.fit(X, y) # 模型训练模型预测与训练
x_test X.iloc[[0, 1, 50, 51, 100, 120], :]
y_test y.iloc[[0, 1, 50, 51, 100, 120]] # 只有一列y_pred model.predict(x_test)模型评估
# 输出原始值和真实值
df pd.DataFrame()
df[原始值] y_test
df[预测值] y_preddf原始值预测值00.20.2510.20.20501.41.40511.51.501002.52.501202.32.30
from sklearn.metrics import mean_absolute_error
# 误差计算
mse mean_absolute_error(y_test, y_pred)
mse结果
0.008333333333333331# 打印树结构
r tree.export_text(model)
print(r)# 树模型结构比较复杂可以运行后面代码绘图展示。
|--- feature_2 2.45
| |--- feature_1 3.25
| | |--- feature_1 2.60
| | | |--- value: [0.30]
| | |--- feature_1 2.60
| | | |--- feature_0 4.85
| | | | |--- feature_0 4.35
| | | | | |--- value: [0.10]
| | | | |--- feature_0 4.35
| | | | | |--- feature_2 1.35
| | | | | | |--- value: [0.20]
| | | | | |--- feature_2 1.35
| | | | | | |--- feature_1 2.95
| | | | | | | |--- value: [0.20]
| | | | | | |--- feature_1 2.95
| | | | | | | |--- feature_0 4.65
| | | | | | | | |--- value: [0.20]
| | | | | | | |--- feature_0 4.65
| | | | | | | | |--- feature_1 3.05
| | | | | | | | | |--- value: [0.20]
| | | | | | | | |--- feature_1 3.05
| | | | | | | | | |--- value: [0.20]
| | | |--- feature_0 4.85
| | | | |--- feature_0 4.95
| | | | | |--- feature_1 3.05
| | | | | | |--- value: [0.20]
| | | | | |--- feature_1 3.05
| | | | | | |--- value: [0.10]
| | | | |--- feature_0 4.95
| | | | | |--- value: [0.20]
| |--- feature_1 3.25
| | |--- feature_2 1.55
| | | |--- feature_1 4.30
| | | | |--- feature_1 3.95
| | | | | |--- feature_1 3.85
| | | | | | |--- feature_1 3.65
| | | | | | | |--- feature_0 5.30
| | | | | | | | |--- feature_2 1.45
| | | | | | | | | |--- feature_1 3.55
| | | | | | | | | | |--- feature_2 1.35
| | | | | | | | | | | |--- value: [0.30]
| | | | | | | | | | |--- feature_2 1.35
| | | | | | | | | | | |--- truncated branch of depth 3
| | | | | | | | | |--- feature_1 3.55
| | | | | | | | | | |--- value: [0.20]
| | | | | | | | |--- feature_2 1.45
| | | | | | | | | |--- value: [0.20]
| | | | | | | |--- feature_0 5.30
| | | | | | | | |--- feature_1 3.45
| | | | | | | | | |--- value: [0.40]
| | | | | | | | |--- feature_1 3.45
| | | | | | | | | |--- value: [0.20]
| | | | | | |--- feature_1 3.65
| | | | | | | |--- feature_0 5.20
| | | | | | | | |--- feature_1 3.75
| | | | | | | | | |--- value: [0.40]
| | | | | | | | |--- feature_1 3.75
| | | | | | | | | |--- value: [0.30]
| | | | | | | |--- feature_0 5.20
| | | | | | | | |--- value: [0.20]
| | | | | |--- feature_1 3.85
| | | | | | |--- value: [0.40]
| | | | |--- feature_1 3.95
| | | | | |--- feature_0 5.35
| | | | | | |--- value: [0.10]
| | | | | |--- feature_0 5.35
| | | | | | |--- value: [0.20]
| | | |--- feature_1 4.30
| | | | |--- value: [0.40]
| | |--- feature_2 1.55
| | | |--- feature_0 4.90
| | | | |--- value: [0.20]
| | | |--- feature_0 4.90
| | | | |--- feature_0 5.05
| | | | | |--- feature_1 3.45
| | | | | | |--- value: [0.40]
| | | | | |--- feature_1 3.45
| | | | | | |--- value: [0.60]
| | | | |--- feature_0 5.05
| | | | | |--- feature_1 3.35
| | | | | | |--- value: [0.50]
| | | | | |--- feature_1 3.35
| | | | | | |--- feature_2 1.65
| | | | | | | |--- value: [0.20]
| | | | | | |--- feature_2 1.65
| | | | | | | |--- feature_1 3.60
| | | | | | | | |--- value: [0.20]
| | | | | | | |--- feature_1 3.60
| | | | | | | | |--- feature_0 5.55
| | | | | | | | | |--- value: [0.40]
| | | | | | | | |--- feature_0 5.55
| | | | | | | | | |--- value: [0.30]
|--- feature_2 2.45
| |--- feature_2 4.75
| | |--- feature_2 4.15
| | | |--- feature_1 2.65
| | | | |--- feature_2 3.95
| | | | | |--- feature_2 3.75
| | | | | | |--- feature_2 3.15
| | | | | | | |--- value: [1.10]
| | | | | | |--- feature_2 3.15
| | | | | | | |--- value: [1.00]
| | | | | |--- feature_2 3.75
| | | | | | |--- feature_0 5.55
| | | | | | | |--- value: [1.10]
| | | | | | |--- feature_0 5.55
| | | | | | | |--- value: [1.10]
| | | | |--- feature_2 3.95
| | | | | |--- feature_0 5.90
| | | | | | |--- feature_0 5.65
| | | | | | | |--- feature_1 2.40
| | | | | | | | |--- value: [1.30]
| | | | | | | |--- feature_1 2.40
| | | | | | | | |--- value: [1.30]
| | | | | | |--- feature_0 5.65
| | | | | | | |--- value: [1.20]
| | | | | |--- feature_0 5.90
| | | | | | |--- value: [1.00]
| | | |--- feature_1 2.65
| | | | |--- feature_0 5.75
| | | | | |--- feature_0 5.40
| | | | | | |--- value: [1.40]
| | | | | |--- feature_0 5.40
| | | | | | |--- value: [1.30]
| | | | |--- feature_0 5.75
| | | | | |--- feature_2 4.05
| | | | | | |--- feature_2 3.95
| | | | | | | |--- value: [1.20]
| | | | | | |--- feature_2 3.95
| | | | | | | |--- value: [1.30]
| | | | | |--- feature_2 4.05
| | | | | | |--- value: [1.00]
| | |--- feature_2 4.15
| | | |--- feature_2 4.45
| | | | |--- feature_0 5.80
| | | | | |--- feature_1 2.65
| | | | | | |--- value: [1.20]
| | | | | |--- feature_1 2.65
| | | | | | |--- feature_1 2.95
| | | | | | | |--- feature_1 2.80
| | | | | | | | |--- value: [1.30]
| | | | | | | |--- feature_1 2.80
| | | | | | | | |--- value: [1.30]
| | | | | | |--- feature_1 2.95
| | | | | | | |--- value: [1.20]
| | | | |--- feature_0 5.80
| | | | | |--- feature_1 2.95
| | | | | | |--- value: [1.30]
| | | | | |--- feature_1 2.95
| | | | | | |--- feature_0 6.25
| | | | | | | |--- value: [1.50]
| | | | | | |--- feature_0 6.25
| | | | | | | |--- value: [1.40]
| | | |--- feature_2 4.45
| | | | |--- feature_0 5.15
| | | | | |--- value: [1.70]
| | | | |--- feature_0 5.15
| | | | | |--- feature_1 3.25
| | | | | | |--- feature_1 2.95
| | | | | | | |--- feature_2 4.65
| | | | | | | | |--- feature_0 5.85
| | | | | | | | | |--- value: [1.30]
| | | | | | | | |--- feature_0 5.85
| | | | | | | | | |--- feature_0 6.55
| | | | | | | | | | |--- value: [1.50]
| | | | | | | | | |--- feature_0 6.55
| | | | | | | | | | |--- value: [1.30]
| | | | | | | |--- feature_2 4.65
| | | | | | | | |--- feature_1 2.85
| | | | | | | | | |--- value: [1.20]
| | | | | | | | |--- feature_1 2.85
| | | | | | | | | |--- value: [1.40]
| | | | | | |--- feature_1 2.95
| | | | | | | |--- feature_2 4.55
| | | | | | | | |--- value: [1.50]
| | | | | | | |--- feature_2 4.55
| | | | | | | | |--- feature_2 4.65
| | | | | | | | | |--- value: [1.40]
| | | | | | | | |--- feature_2 4.65
| | | | | | | | | |--- feature_1 3.15
| | | | | | | | | | |--- value: [1.50]
| | | | | | | | | |--- feature_1 3.15
| | | | | | | | | | |--- value: [1.40]
| | | | | |--- feature_1 3.25
| | | | | | |--- value: [1.60]
| |--- feature_2 4.75
| | |--- feature_2 5.05
| | | |--- feature_0 6.75
| | | | |--- feature_0 5.80
| | | | | |--- value: [2.00]
| | | | |--- feature_0 5.80
| | | | | |--- feature_1 2.35
| | | | | | |--- value: [1.50]
| | | | | |--- feature_1 2.35
| | | | | | |--- feature_0 6.25
| | | | | | | |--- value: [1.80]
| | | | | | |--- feature_0 6.25
| | | | | | | |--- feature_2 4.95
| | | | | | | | |--- feature_1 2.60
| | | | | | | | | |--- value: [1.50]
| | | | | | | | |--- feature_1 2.60
| | | | | | | | | |--- value: [1.80]
| | | | | | | |--- feature_2 4.95
| | | | | | | | |--- feature_0 6.50
| | | | | | | | | |--- value: [1.90]
| | | | | | | | |--- feature_0 6.50
| | | | | | | | | |--- value: [1.70]
| | | |--- feature_0 6.75
| | | | |--- feature_0 6.85
| | | | | |--- value: [1.40]
| | | | |--- feature_0 6.85
| | | | | |--- value: [1.50]
| | |--- feature_2 5.05
| | | |--- feature_1 3.05
| | | | |--- feature_0 6.35
| | | | | |--- feature_0 5.85
| | | | | | |--- feature_1 2.75
| | | | | | | |--- value: [1.90]
| | | | | | |--- feature_1 2.75
| | | | | | | |--- value: [2.40]
| | | | | |--- feature_0 5.85
| | | | | | |--- feature_1 2.85
| | | | | | | |--- feature_1 2.65
| | | | | | | | |--- value: [1.40]
| | | | | | | |--- feature_1 2.65
| | | | | | | | |--- feature_1 2.75
| | | | | | | | | |--- value: [1.60]
| | | | | | | | |--- feature_1 2.75
| | | | | | | | | |--- value: [1.50]
| | | | | | |--- feature_1 2.85
| | | | | | | |--- feature_1 2.95
| | | | | | | | |--- value: [1.80]
| | | | | | | |--- feature_1 2.95
| | | | | | | | |--- value: [1.80]
| | | | |--- feature_0 6.35
| | | | | |--- feature_0 7.50
| | | | | | |--- feature_0 7.15
| | | | | | | |--- feature_1 2.75
| | | | | | | | |--- feature_0 6.55
| | | | | | | | | |--- value: [1.90]
| | | | | | | | |--- feature_0 6.55
| | | | | | | | | |--- value: [1.80]
| | | | | | | |--- feature_1 2.75
| | | | | | | | |--- feature_0 6.60
| | | | | | | | | |--- feature_2 5.55
| | | | | | | | | | |--- feature_2 5.35
| | | | | | | | | | | |--- value: [2.00]
| | | | | | | | | | |--- feature_2 5.35
| | | | | | | | | | | |--- value: [1.80]
| | | | | | | | | |--- feature_2 5.55
| | | | | | | | | | |--- feature_2 5.70
| | | | | | | | | | | |--- value: [2.15]
| | | | | | | | | | |--- feature_2 5.70
| | | | | | | | | | | |--- value: [2.20]
| | | | | | | | |--- feature_0 6.60
| | | | | | | | | |--- feature_0 6.75
| | | | | | | | | | |--- value: [2.30]
| | | | | | | | | |--- feature_0 6.75
| | | | | | | | | | |--- value: [2.10]
| | | | | | |--- feature_0 7.15
| | | | | | | |--- feature_2 5.95
| | | | | | | | |--- value: [1.60]
| | | | | | | |--- feature_2 5.95
| | | | | | | | |--- feature_1 2.85
| | | | | | | | | |--- value: [1.90]
| | | | | | | | |--- feature_1 2.85
| | | | | | | | | |--- value: [1.80]
| | | | | |--- feature_0 7.50
| | | | | | |--- feature_2 6.80
| | | | | | | |--- feature_2 6.35
| | | | | | | | |--- value: [2.30]
| | | | | | | |--- feature_2 6.35
| | | | | | | | |--- feature_1 2.90
| | | | | | | | | |--- value: [2.00]
| | | | | | | | |--- feature_1 2.90
| | | | | | | | | |--- value: [2.10]
| | | | | | |--- feature_2 6.80
| | | | | | | |--- value: [2.30]
| | | |--- feature_1 3.05
| | | | |--- feature_1 3.25
| | | | | |--- feature_2 5.95
| | | | | | |--- feature_0 6.60
| | | | | | | |--- feature_2 5.40
| | | | | | | | |--- feature_0 6.45
| | | | | | | | | |--- value: [2.30]
| | | | | | | | |--- feature_0 6.45
| | | | | | | | | |--- value: [2.00]
| | | | | | | |--- feature_2 5.40
| | | | | | | | |--- value: [1.80]
| | | | | | |--- feature_0 6.60
| | | | | | | |--- feature_2 5.50
| | | | | | | | |--- feature_2 5.25
| | | | | | | | | |--- value: [2.30]
| | | | | | | | |--- feature_2 5.25
| | | | | | | | | |--- value: [2.10]
| | | | | | | |--- feature_2 5.50
| | | | | | | | |--- feature_2 5.65
| | | | | | | | | |--- value: [2.40]
| | | | | | | | |--- feature_2 5.65
| | | | | | | | | |--- value: [2.30]
| | | | | |--- feature_2 5.95
| | | | | | |--- value: [1.80]
| | | | |--- feature_1 3.25
| | | | | |--- feature_0 7.45
| | | | | | |--- feature_2 5.85
| | | | | | | |--- feature_2 5.65
| | | | | | | | |--- feature_2 5.50
| | | | | | | | | |--- value: [2.30]
| | | | | | | | |--- feature_2 5.50
| | | | | | | | | |--- value: [2.40]
| | | | | | | |--- feature_2 5.65
| | | | | | | | |--- value: [2.30]
| | | | | | |--- feature_2 5.85
| | | | | | | |--- feature_0 6.75
| | | | | | | | |--- value: [2.50]
| | | | | | | |--- feature_0 6.75
| | | | | | | | |--- value: [2.50]
| | | | | |--- feature_0 7.45
| | | | | | |--- feature_0 7.80
| | | | | | | |--- value: [2.20]
| | | | | | |--- feature_0 7.80
| | | | | | | |--- value: [2.00]
树图绘制
from sklearn.tree import export_graphviz
import graphviz#设置字体
from pylab import mpl
mpl.rcParams[font.sans-serif] [SimHei] # 显示中文# 使用export_graphviz生成DOT文件
dot_data export_graphviz(model, out_fileNone, feature_names[花萼-width, 花萼-length, 花瓣-width], class_names[花瓣-length],filledTrue, roundedTrue,special_charactersTrue) # 使用graphviz渲染DOT文件
graph graphviz.Source(dot_data)
graph.render(decision_tree) # 将图形保存为PDF或其它格式
graph.view() # 在默认查看器中打开图形图太长了不方便展示可以运行代码绘制。
ue: [2.50] | | | | | | | |— feature_0 6.75 | | | | | | | | |— value: [2.50] | | | | | |— feature_0 7.45 | | | | | | |— feature_0 7.80 | | | | | | | |— value: [2.20] | | | | | | |— feature_0 7.80 | | | | | | | |— value: [2.00] ## 树图绘制python
from sklearn.tree import export_graphviz
import graphviz#设置字体
from pylab import mpl
mpl.rcParams[font.sans-serif] [SimHei] # 显示中文# 使用export_graphviz生成DOT文件
dot_data export_graphviz(model, out_fileNone, feature_names[花萼-width, 花萼-length, 花瓣-width], class_names[花瓣-length],filledTrue, roundedTrue,special_charactersTrue) # 使用graphviz渲染DOT文件
graph graphviz.Source(dot_data)
graph.render(decision_tree) # 将图形保存为PDF或其它格式
graph.view() # 在默认查看器中打开图形图太长了不方便展示可以运行代码绘制。
