KNeighborsClassifierreturns的概率预测方法只有0和1

Question

谁能告诉我我的代码有什么问题？为什么我可以使用 LinearRegression 预测鸢尾花数据集的概率，但是 KNeighborsClassifier 给了我 0 或 1 而它应该给我一个像 LinearRegression 那样的结果？

from sklearn.datasets import load_iris
from sklearn import metrics

iris = load_iris()
X = iris.data
y = iris.target

for train_index, test_index in skf:
    X_train, X_test = X_total[train_index], X_total[test_index]
    y_train, y_test = y_total[train_index], y_total[test_index]

from sklearn.linear_model import LogisticRegression
ln = LogisticRegression()
ln.fit(X_train,y_train)

ln.predict_proba(X_test)[:,1]

array([ 0.18075722, 0.08906078, 0.14693156, 0.10467766, 0.14823032, 0.70361962, 0.65733216, 0.77864636, 0.67203114, 0.68655163, 0.25219798, 0.3863194 , 0.30735105, 0.13963637, 0.28017798])

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5, algorithm='ball_tree', metric='euclidean')
knn.fit(X_train, y_train)

knn.predict_proba(X_test)[0:10,1]

array([ 0., 0., 0., 0., 0., 1., 1., 1., 1., 1.])

Answer 1

因为KNN的概率概念非常有限。它的估计只是最近邻居中选票的分数。将邻居数量增加到 15 或 100 或决策边界附近的查询点，您将看到更多不同的结果。目前，您的点总是有 5 个相同标签的邻居（因此概率为 0 或 1）。

Answer 2

在这里，我有一个 knn 模型 - model_knn

使用 sklearn

result = {}    
model_classes = model_knn.classes_
predicted = model_knn.predict(word_average)
score = model_knn.predict_proba(word_average)
index = np.where(model_classes == predicted[0])[0][0]
result["predicted"] = predicted[0]
result["score"] = score[0][index]

KNeighborsClassifierreturns的概率预测方法只有0和1

Probability prediction method of KNeighborsClassifier returns only 0 and 1

machine-learning

probability

nearest-neighbor

scikit-learn