【红酒数据集】机器学习之决策树在sklearn中的实现

大家好~ O (o)。首先，我的开发环境是Jupyter实验室，请参考使用的库和版本。(注：)。

python 3 . 7 . 1(您的版本必须至少为3.4

Scikit-learn 0.20.0(您的版本必须至少为0.20

Graphviz 0.8.4(无法绘制决策树。安装代码conda install python-graphviz

Numpy 1.15.3、pandas 0.23.4、matplotlib 3.0.1、scipy 1.1.0

用SKlearn创建决策树

这里使用的数据集是SKlearn的葡萄酒数据集。

1 导入需要的算法库和模块

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="Python" contenteditable="true" cid="n11" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">from sklearn import tree #导入tree模块

from import load_wine #导入红酒数据集

from import train_test_split #导入训练集和测试集切分包</pre>

2 探索数据

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="python " contenteditable="true" cid="n13" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">wine = load_wine()

wine.data

wine.da

wine.target

wine.;/pre>

运行的结果是这样子的：

data就是该数据集的特征矩阵，从运行结果可以看出，该红酒数据集一共有178条记录，13个特征。

特征矩阵中有178条记录，相对应的标签Y就有178个数据。

如果wine是一张表，应该长这样：

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="Python" contenteditable="true" cid="n20" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">import pandas as pd

([),)],axis=1)</pre>

这是数据集特征列名和标签分类

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="python" contenteditable="true" cid="n23" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">wine.feature_names

wine.target_names</pre>

3 分训练集和测试集

这里选取30%作为测试集。切分好之后，训练集有124条数据，测试集有54条数据。

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="Python" contenteditable="true" cid="n28" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">Xtrain, Xtest, Ytrain, Ytest = train_test_split,wine.target,test_size=0.3)

X

X;/pre>

4 建立模型

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="Python" contenteditable="true" cid="n32" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">clf = (criterion="entropy") #初始化树模型

clf = clf.fit(Xtrain, Ytrain) #实例化训练集

score = clf.score(Xtest, Ytest) #返回预测的准确度

​

score</pre>

5 画出一棵树吧

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="Python" contenteditable="true" cid="n36" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">feature_name = ['酒精','苹果酸','灰','灰的碱性','镁','总酚','类黄酮','非黄烷类酚类','花青素','颜色强度','色调','od280/od315稀释葡萄酒','脯氨酸']

​

import graphviz

dot_data = (clf

,out_file=None

,feature_names= feature_name

,class_names=["琴酒","雪莉","贝尔摩德"]

,filled=True

,rounded=True

)

graph = gra(dot_data)

graph</pre>

6 探索决策树

<pre spellcheck="false" class="md-fences md-end-block ty-contain-cm modeLoaded" lang="Python" contenteditable="true" cid="n40" mdtype="fences" style="box-sizing: border-box; overflow: visible; font-family: var(--monospace); font-size: 0.9em; display: block; break-inside: avoid; text-align: left; white-space: normal; background-image: inherit; background-position: inherit; background-size: inherit; background-repeat: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: rgb(248, 248, 248); position: relative !important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit; color: rgb(51, 51, 51); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">#特征重要性

clf.feature_importances_

​

[*zip(feature_name,clf.feature_importances_)]</pre>

到现在为止，我们已经学会建立一棵完整的决策树了。有兴趣的话，动手建立一棵属于自己的决策树吧~