TypeError Traceback (most recent call last)
Cell In[140], line 3
1 from sklearn import linear_model
2 model = linear_model.LinearRegression()
----> 3 model.fit(X,y)
File ~\anaconda3\Lib\site-packages\sklearn\base.py:1474, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
1467 estimator._validate_params()
1469 with config_context(
1470 skip_parameter_validation=(
1471 prefer_skip_nested_validation or global_skip_validation
1472 )
1473 ):
-> 1474 return fit_method(estimator, *args, **kwargs)
File ~\anaconda3\Lib\site-packages\sklearn\linear_model\_base.py:578, in LinearRegression.fit(self, X, y, sample_weight)
574 n_jobs_ = self.n_jobs
576 accept_sparse = False if self.positive else ["csr", "csc", "coo"]
--> 578 X, y = self._validate_data(
579 X, y, accept_sparse=accept_sparse, y_numeric=True, multi_output=True
580 )
582 has_sw = sample_weight is not None
583 if has_sw:
File ~\anaconda3\Lib\site-packages\sklearn\base.py:650, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)
648 y = check_array(y, input_name="y", **check_y_params)
649 else:
--> 650 X, y = check_X_y(X, y, **check_params)
651 out = X, y
653 if not no_val_X and check_params.get("ensure_2d", True):
File ~\anaconda3\Lib\site-packages\sklearn\utils\validation.py:1263, in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
1258 estimator_name = _check_estimator_name(estimator)
1259 raise ValueError(
1260 f"{estimator_name} requires y to be passed, but the target y is None"
1261 )
-> 1263 X = check_array(
1264 X,
1265 accept_sparse=accept_sparse,
1266 accept_large_sparse=accept_large_sparse,
1267 dtype=dtype,
1268 order=order,
1269 copy=copy,
1270 force_all_finite=force_all_finite,
1271 ensure_2d=ensure_2d,
1272 allow_nd=allow_nd,
1273 ensure_min_samples=ensure_min_samples,
1274 ensure_min_features=ensure_min_features,
1275 estimator=estimator,
1276 input_name="X",
1277 )
1279 y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric, estimator=estimator)
1281 check_consistent_length(X, y)
File ~\anaconda3\Lib\site-packages\sklearn\utils\validation.py:833, in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)
734 """Input validation on an array, list, sparse matrix or similar.
735
736 By default, the input is checked to be a non-empty 2D array containing
(...)
830 array([[1, 2, 3], [4, 5, 6]])
831 """
832 if isinstance(array, np.matrix):
--> 833 raise TypeError(
834 "np.matrix is not supported. Please convert to a numpy array with "
835 "np.asarray. For more information see: "
836 "https://numpy.org/doc/stable/reference/generated/numpy.matrix.html"
837 )
839 xp, is_array_api_compliant = get_namespace(array)
841 # store reference to original array to check if copy is needed when
842 # function returns
TypeError: np.matrix is not supported. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html