Question

我试图将kNN算法测试结果的准确性与使用Matlab绘图的chi2传递的特征数量进行对比，但我不确定如何提取分数以使其在y轴上。事实上，我知道传递的特征数量应该在x轴上。

它还会打印出具有相应功能的最高分。

我的代码在

下面

import numpy as np
import pandas as pd
from sklearn.datasets import make_classification
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

X, y = make_classification(n_samples=50,
                           n_features=6,
                           n_informative=3,
                           n_classes=2,
                           random_state=10,
                           shuffle=True)


# 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})

df = df.abs()
y = df['Class']
X = df.drop('Class', axis=1)



i = 0

for i in range(1,6):
    i = i +1

    X_new = SelectKBest(chi2, k=i).fit_transform(X, y)

    X_train, X_test, y_train, y_test = train_test_split(X_new ,y, 
                                                    stratify=y, 
                                                    random_state=66, 
                                                    test_size=0.3, 
                                                    shuffle = True)


    clf = KNeighborsClassifier()

    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)



#plt.xlabel("Number of features selected")
#plt.ylabel("Test Accuracy Score")

Answer 1

您可以使用函数sklearn.metrics.accuracy_score计算测试集的准确度。然后将此精度存储在每个循环迭代的列表中。

from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)

用于kNN分类器的Matplotlib图

1 个答案: