本文整理汇总了Python中sklearn.tree.DecisionTreeClassifier.random_state方法的典型用法代码示例。如果您正苦于以下问题：Python DecisionTreeClassifier.random_state方法的具体用法？Python DecisionTreeClassifier.random_state怎么用？Python DecisionTreeClassifier.random_state使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.tree.DecisionTreeClassifier的用法示例。
在下文中一共展示了DecisionTreeClassifier.random_state方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: t3
# 需要导入模块: from sklearn.tree import DecisionTreeClassifier [as 别名]
# 或者: from sklearn.tree.DecisionTreeClassifier import random_state [as 别名]
def t3():
import pandas
df = pandas.read_csv('w1/train.csv', index_col='PassengerId')
d = df[['Pclass', 'Fare', 'Age', 'Sex', 'Survived']].dropna().replace('male', 1).replace('female', 0)
# print(d1)
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier()
clf.random_state = 241
clf.fit(d[['Pclass', 'Fare', 'Age', 'Sex']], d['Survived'])
importances = clf.feature_importances_
print(importances)
# [ 0.13700004 0.31259037 0.24989737 0.30051221]
# [ 0.14000522 0.30343647 0.2560461 0.30051221]
pf('2', 'Fare Sex')示例2: DecisionTreeClassifier
# 需要导入模块: from sklearn.tree import DecisionTreeClassifier [as 别名]
# 或者: from sklearn.tree.DecisionTreeClassifier import random_state [as 别名]
import pandas
import re
import numpy as np
from sklearn.tree import DecisionTreeClassifier
data = pandas.read_csv("C:\\Users\\Vladislav\\Desktop\\Machine Learning\\titanic.csv", index_col="PassengerId")
data1 = data.loc[ :,['Pclass', 'Fare', 'Age', 'Sex', 'Survived']]
data2 = data1.dropna()
SexN = data2.Sex.factorize()
data2['SexN'] = SexN[0]
X = data2.loc[ :,['Pclass', 'Fare', 'Age', 'SexN']]
y = data2['Survived']
#X = np.array([[1, 2], [3, 4], [5, 6]])
#y = np.array([0, 1, 0])
clf = DecisionTreeClassifier()
clf.random_state = 241
clf.fit(X, y)
importances = clf.feature_importances_