在使用iris数据集的knn中,重加权距离返回与常规距离相同的结果

我正在试验距离上的权重如何影响kNN算法的性能,对于一个可复制的示例,我正在使用iris数据集。

令我惊讶的是,加权2个预测值比其余2个预测值高100倍,生成了与未加权模型相同的预测。这一违反直觉的发现是什么?

我的代码如下:

X_original = iris['data']
Y = iris['target']

sc = StandardScaler() # Defines the parameters of the Scaler

X = sc.fit_transform(X_original)  # Transforms the original data to standardized data and returns them

from sklearn.model_selection import StratifiedShuffleSplit

sss = StratifiedShuffleSplit(n_splits = 1, train_size = 0.8, test_size = 0.2)

split = sss.split(X, Y)

s = list(split)

train_index = s[0][0]

test_index = s[0][1]

X_train = X[train_index, ] 

X_test = X[test_index, ] 

Y_train = Y[train_index] 

Y_test = Y[test_index] 

from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors = 6)

iris_fit = knn.fit(X_train, Y_train)  # The data can be passed as numpy arrays or pandas dataframes/series.
                                                  # All the data should be numeric
                                                  # There should be no NaNs

predictions_w1 = knn.predict(X_test)

weights = np.array([1, 1, 100, 100])
weights =weights/np.sum(weights)

knn_w = KNeighborsClassifier(n_neighbors = 6, metric='wminkowski', p=2, 
                           metric_params={'w': weights})

iris_fit_w = knn_w.fit(X_train, Y_train)  # The data can be passed as numpy arrays or pandas dataframes/series.
                                                  # All the data should be numeric
                                                  # There should be no NaNs

predictions_w100 = knn_w.predict(X_test)

(predictions_w1 != predictions_w100).sum()
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