python - 虹膜模型不同K值的KNN模型的统计指标？

Question

我编写了一些Python代码来将著名的鸢尾花数据集与KNN模型相匹配，我尝试使用不同的k值，如k=2、k=3、k=5，根据我对这些不同k值的理解，混淆矩阵，准确率得分和分类报告值应该不同，但是，无论我给出什么 k 值，输出的统计指标都是相同的，而且“精度”、“召回率”和“f1-score”都是 1.00，如下所示在快照中codes and output 。我在这里错过了什么吗？谢谢!

from sklearn.model_selection import train_test_split

# first split the dataset into its attributes and labels
X = data.iloc[:, :-1].values  
y = data.iloc[:, 4].values 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, 
random_state=42)

from sklearn.neighbors import KNeighborsClassifier

# Instantiate learning model (k = 5)
clf = KNeighborsClassifier(n_neighbors=5)
# Fitting the model
clf.fit(X_train, y_train)
# Predicting the Test set results
y_pred = clf.predict(X_test)
print(y_pred)

from sklearn.metrics import classification_report, confusion_matrix, accuracy_score 

print(confusion_matrix(y_test, y_pred))
print(accuracy_score(y_test, y_pred))
print("classification report:---------------------------\n")
print(classification_report(y_test, y_pred, labels=iris.target))

Answer 1

很可能您在加载数据集并将其拆分为 X 和 y 时犯了错误。检查这个修正。它给出了正确的结果。然而，iris 数据集非常简单，没有太多内容 multicollinearity或heteroscedasticity 。这意味着 knn 在对它们进行完美分类时不会遇到太多麻烦，并且通过更改 knn 参数您不会看到输出指标有太大差异。为了观察剧烈的变化，你应该选择难度较高的数据。

from sklearn.model_selection import train_test_split
from sklearn import datasets

iris = datasets.load_iris()

# import some data to play with
X = iris.data[:, :2]  # we only take the first two features.
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, 
random_state=42)

from sklearn.neighbors import KNeighborsClassifier

# Instantiate learning model (k = 5)
clf = KNeighborsClassifier(n_neighbors=10)
# Fitting the model
clf.fit(X_train, y_train)
# Predicting the Test set results
y_pred = clf.predict(X_test)
print(y_pred)

from sklearn.metrics import classification_report, confusion_matrix, accuracy_score 

print(confusion_matrix(y_test, y_pred))
print(accuracy_score(y_test, y_pred))
print("classification report:---------------------------\n")
print(classification_report(y_test, y_pred, labels=iris.target))

这表明

[1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 2 1 1 1 2 0 1 0 2 2 1 1 2 0 0 0 0 2 0 0 1 2
 0 0 0 1 2 2 0 0]
[[19  0  0]
 [ 0  8  5]
 [ 0  6  7]]
0.7555555555555555
classification report:---------------------------
...

当您调整 knn 参数时，精度确实会发生变化。