【阿旭机器学习实战】【36】糖尿病预测—决策树建模及其可视化

1. 导入数据并查看数据

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# 导入数据包
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split 
from sklearn import metrics 
import matplotlib.pyplot as plt
import matplotlib as matplot
import seaborn as sns
%matplotlib inline

col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label']
df = pd.read_csv("pima-indians-diabetes.csv", header=None, names=col_names)

df.head()

	pregnant	glucose	bp	skin	insulin	bmi	pedigree	age	label
0	6	148	72	35	0	33.6	0.627	50	1
1	1	85	66	29	0	26.6	0.351	31	0
2	8	183	64	0	0	23.3	0.672	32	1
3	1	89	66	23	94	28.1	0.167	21	0
4	0	137	40	35	168	43.1	2.288	33	1

# 相关性矩阵
corr = df.iloc[:,:-1].corr()
#corr = (corr)
sns.heatmap(corr, 
            xticklabels=corr.columns.values,
            yticklabels=corr.columns.values)

corr

	pregnant	glucose	bp	skin	insulin	bmi	pedigree	age
pregnant	1.000000	0.129459	0.141282	-0.081672	-0.073535	0.017683	-0.033523	0.544341
glucose	0.129459	1.000000	0.152590	0.057328	0.331357	0.221071	0.137337	0.263514
bp	0.141282	0.152590	1.000000	0.207371	0.088933	0.281805	0.041265	0.239528
skin	-0.081672	0.057328	0.207371	1.000000	0.436783	0.392573	0.183928	-0.113970
insulin	-0.073535	0.331357	0.088933	0.436783	1.000000	0.197859	0.185071	-0.042163
bmi	0.017683	0.221071	0.281805	0.392573	0.197859	1.000000	0.140647	0.036242
pedigree	-0.033523	0.137337	0.041265	0.183928	0.185071	0.140647	1.000000	0.033561
age	0.544341	0.263514	0.239528	-0.113970	-0.042163	0.036242	0.033561	1.000000

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2. 训练决策树模型及其可视化

# 选择预测所需的特征
feature_cols = ['pregnant', 'insulin', 'bmi', 'age','glucose','bp','pedigree']
X = pima[feature_cols] # 特征
y = pima.label # 类别标签

# 将数据分为训练和测试数据
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test

2.1 决策树模型

# 创建决策树分类器
clf = DecisionTreeClassifier(criterion='entropy')

# 训练模型
clf = clf.fit(X_train,y_train)

# 使用训练好的模型做预测
y_pred = clf.predict(X_test)

# 模型的准确性
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

Accuracy: 0.7489177489177489

2.2 可视化训练好的决策树模型

注意: 需要使用如下命令安装额外两个包用于画决策树的图
conda install python-graphviz
conda install pydotplus

from sklearn.tree import export_graphviz
from six import StringIO 
from IPython.display import Image  
import pydotplus
from sklearn import tree

dot_data = StringIO()
export_graphviz(clf, out_file=dot_data,  
                filled=True, rounded=True,
                special_characters=True,feature_names = feature_cols,class_names=['0','1'])
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  
graph.write_png('diabetes.png')
Image(graph.create_png())

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# 创建新的决策树, 限定树的最大深度, 减少过拟合
clf = tree.DecisionTreeClassifier(
    criterion='entropy',
    max_depth=4, # 定义树的深度, 可以用来防止过拟合
    min_weight_fraction_leaf=0.01 # 定义叶子节点最少需要包含多少个样本(使用百分比表达), 防止过拟合
    )

# 训练模型
clf.fit(X_train,y_train)

# 预测
y_pred = clf.predict(X_test)

# 模型的性能
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

Accuracy: 0.7705627705627706

from six import StringIO  
from IPython.display import Image  
from sklearn.tree import export_graphviz
import pydotplus
dot_data = StringIO()
export_graphviz(clf, out_file=dot_data,  
                filled=True, rounded=True,
                special_characters=True, feature_names = feature_cols,class_names=['0','1'])
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())

graph.write_png('diabetes2.png')
Image(graph.create_png())

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2.2 使用随机森林模型

from sklearn.ensemble import RandomForestClassifier

# 随机森林, 通过调整参数来获取更好的结果
rf = RandomForestClassifier(
    criterion='entropy',
    n_estimators=1, 
    max_depth=5, # 定义树的深度, 可以用来防止过拟合
    min_samples_split=10, # 定义至少多少个样本的情况下才继续分叉
    #min_weight_fraction_leaf=0.02 # 定义叶子节点最少需要包含多少个样本(使用百分比表达), 防止过拟合
    )

# 训练模型
rf.fit(X_train, y_train)

# 做预测
y_pred = rf.predict(X_test)

# 模型的准确率
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))