Ошибка in predict X = self._validate_X_predict(X, check_input)

Всем общий джиги. Изучаю библиотеку sklearn(python) на среде Pycharm(Путь-каталог весь правильный просто на всякий убрал)

Сам код снизу:

Ошибка с низу:
The prediction accuracy is: 92.31%
Traceback (most recent call last):
File "C:\Users", line 52, in <module>
prediction = tree.predict(features)[0]
^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users", line 505, in predict
X = self._validate_X_predict(X, check_input)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users", line 471, in _validate_X_predict
X = self._validate_data(X, dtype=DTYPE, accept_sparse="csr", reset=False)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users", line 577, in _validate_data
X = check_array(X, input_name="X", **check_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^
File "C:\Users", line 899, in check_array
_assert_all_finite(
File "C:\Users", line 146, in _assert_all_finite
raise ValueError(msg_err)
ValueError: Input X contains NaN.
DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingCla ssifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stabl... mpute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stabl... nan-values

Process finished with exit code 1

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FARA7182, ну то же самое)

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FARA7182, вопрос в чем? перевести за тебя в гугле?

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