相关疑难解决方法(0)

嗨,下面的人是决策树的片段,因为它非常庞大.

当节点中的最低值小于5 时,如何使树停止增长.以下是生成决策树的代码.在SciKit - Decission Tree上我们可以看到唯一的方法是通过min_impurity_decrease,但我不确定它是如何具体工作的.

import numpy as np
import pandas as pd
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier


X, y = make_classification(n_samples=1000,
                           n_features=6,
                           n_informative=3,
                           n_classes=2,
                           random_state=0,
                           shuffle=False)

# Creating a dataFrame
df = pd.DataFrame({'Feature 1':X[:,0],
                                  'Feature 2':X[:,1],
                                  'Feature 3':X[:,2],
                                  'Feature 4':X[:,3],
                                  'Feature 5':X[:,4],
                                  'Feature 6':X[:,5],
                                  'Class':y})


y_train = df['Class']
X_train = df.drop('Class',axis = 1)

dt = DecisionTreeClassifier( random_state=42)                
dt.fit(X_train, y_train)

from IPython.display import display, Image
import …