有谁能告诉我代码的问题是什么？ 为什么我可以通过使用LinearRegression来预测虹膜数据集的概率，但是KNeighborsClassifier给我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， 0.70361962,0.66533216,0.77864636,0.67203114,0.68655163， 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]
数组（[0,0,0,0,0,0,1,1,1,1,1,1。]）
答案 0 :(得分：6)
因为KNN的概率概念非常有限。它的估计只是最近邻居的投票比例。将邻居数量增加到15或100或决策边界附近的查询点，您将看到更多样化的结果。目前，您的积分总是只有5个相同标签的邻居（因此概率为0或1）。
答案 1 :(得分：0)
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]