【阿旭机器学习实战】【25】决策树模型----树叶分类实战

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本文通过构建决策树模型，对某树叶分类数据集进行建模预测，并进行模型优化。

决策树进行树叶分类实战

1. 导入数据

import pandas as pd
import matplotlib.pyplot as plt

from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection  import GridSearchCV

data = pd.read_csv('train.csv')

data.head()

	id	species	margin1	margin2	margin3	margin4	margin5	margin6	margin7	margin8	...	texture55	texture56	texture57	texture58	texture59	texture60	texture61	texture62	texture63	texture64
0	1	Acer_Opalus	0.007812	0.023438	0.023438	0.003906	0.011719	0.009766	0.027344	0.0	...	0.007812	0.000000	0.002930	0.002930	0.035156	0.0	0.0	0.004883	0.000000	0.025391
1	2	Pterocarya_Stenoptera	0.005859	0.000000	0.031250	0.015625	0.025391	0.001953	0.019531	0.0	...	0.000977	0.000000	0.000000	0.000977	0.023438	0.0	0.0	0.000977	0.039062	0.022461
2	3	Quercus_Hartwissiana	0.005859	0.009766	0.019531	0.007812	0.003906	0.005859	0.068359	0.0	...	0.154300	0.000000	0.005859	0.000977	0.007812	0.0	0.0	0.000000	0.020508	0.002930
3	5	Tilia_Tomentosa	0.000000	0.003906	0.023438	0.005859	0.021484	0.019531	0.023438	0.0	...	0.000000	0.000977	0.000000	0.000000	0.020508	0.0	0.0	0.017578	0.000000	0.047852
4	6	Quercus_Variabilis	0.005859	0.003906	0.048828	0.009766	0.013672	0.015625	0.005859	0.0	...	0.096680	0.000000	0.021484	0.000000	0.000000	0.0	0.0	0.000000	0.000000	0.031250

5 rows × 194 columns

数据说明：
species类别，64个margin边缘特征，64个shape形状特征，64个texture质感特征

一共有99个树叶类别

data.shape

(990, 194)

# 查看树叶类别数
len(data.species.unique())

99

2. 特征工程

# 把字符串类别转化为数字形式
lb = LabelEncoder().fit(data.species) 
labels = lb.transform(data.species)    
# 去掉'species', 'id'这两列对于训练模型无用的列
data = data.drop(['species', 'id'], axis=1)  
data.head()

	margin1	margin2	margin3	margin4	margin5	margin6	margin7	margin8	margin9	margin10	...	texture55	texture56	texture57	texture58	texture59	texture60	texture61	texture62	texture63	texture64
0	0.007812	0.023438	0.023438	0.003906	0.011719	0.009766	0.027344	0.0	0.001953	0.033203	...	0.007812	0.000000	0.002930	0.002930	0.035156	0.0	0.0	0.004883	0.000000	0.025391
1	0.005859	0.000000	0.031250	0.015625	0.025391	0.001953	0.019531	0.0	0.000000	0.007812	...	0.000977	0.000000	0.000000	0.000977	0.023438	0.0	0.0	0.000977	0.039062	0.022461
2	0.005859	0.009766	0.019531	0.007812	0.003906	0.005859	0.068359	0.0	0.000000	0.044922	...	0.154300	0.000000	0.005859	0.000977	0.007812	0.0	0.0	0.000000	0.020508	0.002930
3	0.000000	0.003906	0.023438	0.005859	0.021484	0.019531	0.023438	0.0	0.013672	0.017578	...	0.000000	0.000977	0.000000	0.000000	0.020508	0.0	0.0	0.017578	0.000000	0.047852
4	0.005859	0.003906	0.048828	0.009766	0.013672	0.015625	0.005859	0.0	0.000000	0.005859	...	0.096680	0.000000	0.021484	0.000000	0.000000	0.0	0.0	0.000000	0.000000	0.031250

5 rows × 192 columns

labels[:5]

array([ 3, 49, 65, 94, 84], dtype=int64)

# 切分数据集
x_train,x_test,y_train,y_test = train_test_split(data, labels, test_size=0.2, stratify=labels)

3. 构建决策树模型

tree = DecisionTreeClassifier()
tree.fit(x_train, y_train)

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best')

tree.score(x_test, y_test)

0.6767676767676768

tree.score(x_train, y_train)

1.0

结果表明该模型在训练集准确率为100%，而在测试集准确率仅有67%，存在过拟合现象，模型需要进一步优化。

4. 模型优化

# max_depth:树的最大深度
# min_samples_split:内部节点再划分所需最小样本数
# min_samples_leaf:叶子节点最少样本数
param_grid = {'max_depth': [10,15,20,25,30],
                    'min_samples_split': [2,3,4,5,6,7,8],
                    'min_samples_leaf':[1,2,3,4,5,6,7]}
# 网格搜索
model = GridSearchCV(tree, param_grid, cv=3)
model.fit(x_train, y_train)
print(model.best_estimator_)

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=30,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=4, min_samples_split=5,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best')

model.score(x_train, y_train)

0.9444444444444444

model.score(x_test, y_test)

0.6868686868686869

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