y = df.pitch_name
y = np.array(y)
y = y.reshape(-1, 1)
from sklearn.preprocessing import OrdinalEncoder

ord_enc = OrdinalEncoder()
y = ord_enc.fit_transform(y.reshape(-1, 1))
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=12345
)
knn_model = KNeighborsRegressor(n_neighbors=3)
knn_model.fit(X_train, y_train)
knn_model.predict([X_test[0]])

X has all float value and y is all string type. if I use ordinalEncoder and predict with the model, it works but the issue is that the result I am getting is sometime not a whole number (e.g. 6.3333) when I want to get the exact category.

So whenever I fit the model with the raw categorical value, string, I see this error message TypeError: cannot perform reduce with flexible type. When I check error message, I suppose that the error is happening due to the 238 where they try to get y_pred = np.mean(_y[neigh_ind], axis=1) when it should be median since y is a list of string? any help will be appreciated.

    237         if weights is None:
--> 238             y_pred = np.mean(_y[neigh_ind], axis=1)
    239         else:
    240             y_pred = np.empty((X.shape[0], _y.shape[1]), dtype=np.float64)

CodePudding user response：

Forgive me if I misunderstood something, but it sounds like you are trying to perform classification with a regression model: KNeighborsRegressor.

From here you can see that:

Neighbors-based regression can be used in cases where the data labels are continuous rather than discrete variables. The label assigned to a query point is computed based on the mean of the labels of its nearest neighbors.

So, by using the OrdinalEncoder, you just encoded the float categories, however, afterwards you predicted the mean of the labels of its nearest neighbors, which will not be an integer, and thus not a category.

I suggest that you read this, to learn how to use a KNeighborsClassifier.