python - train_test_split random_state 不起作用；每次都会产生不同的输出

Question

所以，我一直在一组数据上使用 KNN，在 train_test_split 阶段使用 random_state = 4。尽管使用随机状态，但每次输出的准确率、分类报告、预测等都是不同的。想知道为什么会这样吗？

这是数据的头部:(根据all_time_runs和顺序预测位置)

order position  all_time_runs
0     10   NO BAT           1304
1      2  CAN BAT           7396
2      3   NO BAT           6938
3      6  CAN BAT           4903
4      6  CAN BAT           3761

这是分类和预测的代码:

#splitting data into features and target

X = posdf.drop('position',axis=1)
y = posdf['position']   


knn = KNeighborsClassifier(n_neighbors = 5)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)

#fitting the KNN model
knn.fit(X_train, y_train)

#predicting with the model
prediction = knn.predict(X_test)

#knn score
score = knn.score(X_test, y_test)

Answer 1

虽然train_test_split有一个与之相关的随机因素，并且必须解决它以避免产生随机结果，但这并不是您应该解决的唯一因素。

KNN 是一个模型，它采用测试集的每一行，找到最近的 k 个训练集向量，并通过多数决策对其进行分类，即使在平局的情况下，决策也是随机的。您需要set.seed(x)以确保该方法是可复制的。

Documentation状态:

Neighbors-based classification is a type of instance-based learning or non-generalizing learning: it does not attempt to construct a general internal model, but simply stores instances of the training data. Classification is computed from a simple majority vote of the nearest neighbors of each point: a query point is assigned the data class which has the most representatives within the nearest neighbors of the point.