袋装决策树另一个扩展
袋装决策树另一个扩展详细操作教程
这是袋装决策树集成方法的另一个扩展。这种方法是从训练数据集的样本中构造随机树。
在以下Python食谱中，我们将通过在Pima Indians糖尿病数据集上使用sklearn的ExtraTreesClassifier类来构建额外的树集成模型。
首先，如下所示导入所需的程序包-
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
from pandas import read_csv
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import ExtraTreesClassifier
现在，我们需要像之前的示例一样加载Pima糖尿病数据集-
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
path = r"C:\pima-indians-diabetes.csv"
headernames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(path, names = headernames)
array = data.values
X = array[:,0:8]
Y = array[:,8]
接下来，如下所示进行10倍交叉验证的输入-
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
seed = 7
kfold = KFold(n_splits = 10, random_state = seed)
我们需要提供要建造的树木数量。在这里，我们正在构建150棵树木，这些树木的分裂点可以从5个特征中选择-
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
num_trees = 150
max_features = 5
接下来，在以下脚本的帮助下构建模型-
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
model = ExtraTreesClassifier(n_estimators = num_trees, max_features = max_features)
计算并打印结果如下-
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
results = cross_val_score(model, X, Y, cv = kfold)
print(results.mean())
输出
# Filename : example.py
# Copyright : 2020 By Lidihuo
# Author by : www.lidihuo.com
# Date : 2020-08-27
0.7551435406698566
上面的输出表明，我们的袋装额外树木分类器模型的准确度约为75.5％。
