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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。

目录

• 决策树进行树叶分类实战
• 1. 导入数据
• 2. 特征工程
• 3. 构建决策树模型
• 4. 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()

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()

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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