当我运行使用正常精度和 K 折交叉验证的决策树算法时得到两个完全不同的结果

Question

问题是当我运行 DTC 算法时我得到了两个完全不同的结果，我只是想确保我正在编写交叉验证 - K Fold 以正确的方式或理解为什么K折的结果比正常的少太多了。

我已经尝试运行从正常准确度和 K 倍准确度中获取结果的代码，代码如下：

from scipy.signal import butter, lfilter

import numpy as np
import pandas as pd
import pandas
from sklearn import preprocessing
from scipy.fftpack import fft
import pickle
import numpy
from pandas import Series
from numpy.random import randn
import pandas as pd
import numpy as np
from pandas import DataFrame
from sklearn.metrics import accuracy_score

from sklearn.tree import DecisionTreeClassifier
xx = pandas.read_csv("data1.dat", delimiter=",")
y = pandas.read_csv("label.dat", delim_whitespace=True)

x = xx.as_matrix()
y = numpy.array(y).astype(numpy.int)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)



clf2 = DecisionTreeClassifier(random_state=42)
clf2.fit(X_train, y_train)
y_predict_2 = clf2.predict(X_test)
print("DTC Accuracy : ")
print(accuracy_score(y_test, y_predict_2)*100)

DTC 准确度： 97.6302083333333

from sklearn.model_selection import cross_val_score
DTC = DecisionTreeClassifier(random_state=42)
scores =cross_val_score(DTC, x, y, cv=10, scoring='accuracy')
print(scores.mean()*100)

35.331452470904985

from sklearn.model_selection import cross_val_score
DTC = DecisionTreeClassifier(random_state=42)
scores =cross_val_score(DTC, X_train, y_train, cv=10, scoring='accuracy')
print(scores.mean()*100)

97.34356

然而，在交叉验证部分，当我用 X_train 代替 x 和 y_train 代替 y 时，准确率再次上升到 97。我想知道我需要使用 (x and y) 或 (X_train adn y_train) 哪个才是正确的常识交叉验证。

Answer 1

尝试打乱数据并减少交叉验证次数。

import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score
from sklearn.utils import shuffle
from sklearn.tree import DecisionTreeClassifier

xx = pandas.read_csv("data1.dat", delimiter=",")
y = pandas.read_csv("label.dat", delim_whitespace=True)

x = xx.as_matrix()
y = y.values.astype(np.int32).reshape(-1, 1)

x, y = shuffle(x, y, random_state=42)

DTC = DecisionTreeClassifier(random_state=42)
scores = cross_val_score(DTC, x, y, cv=3, scoring='accuracy')
print(scores.mean()*100)

当我运行使用正常精度和 K 折交叉验证的决策树算法时得到两个完全不同的结果

Getting two totally different results when I run Decision Tree Algorithm using normal accuracy and K Fold cross validation

python

sklearn-pandas

当我 运行 使用正常精度和 K 折交叉验证的决策树算法时得到两个完全不同的结果

Getting two totally different results when I run Decision Tree Algorithm using normal accuracy and K Fold cross validation

python

sklearn-pandas

当我运行使用正常精度和 K 折交叉验证的决策树算法时得到两个完全不同的结果