I have an error while fitting the model reg.fit(inputs_train, loan_data_targets_train) ValueError Traceback (most recent call last) <ipython-input-15-966f89d3c717> in <module> ----> 1 reg.fit(inputs_train, loan_data_targets_train) 2 # Estimates the coefficients of the object from the 'LogisticRegression' class 3 # with inputs (independent variables) contained in the first dataframe 4 # and targets (dependent variables) contained in the second dataframe. ~\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py in fit(self, X, y, sample_weight) 1340 _dtype = [np.float64, np.float32] 1341 -> 1342 X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype, 1343 order="C", 1344 accept_large_sparse=solver != 'liblinear') ~\anaconda3\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params) 430 y = check_array(y, **check_y_params) 431 else: --> 432 X, y = check_X_y(X, y, **check_params) 433 out = X, y 434 ~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs) 71 FutureWarning) 72 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)}) ---> 73 return f(**kwargs) 74 return inner_f 75 ~\anaconda3\lib\site-packages\sklearn\utils\validation.py 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) 794 raise ValueError("y cannot be None") 795 --> 796 X = check_array(X, accept_sparse=accept_sparse, 797 accept_large_sparse=accept_large_sparse, 798 dtype=dtype, order=order, copy=copy, ~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs) 71 FutureWarning) 72 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)}) ---> 73 return f(**kwargs) 74 return inner_f 75 ~\anaconda3\lib\site-packages\sklearn\utils\validation.py 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) 643 644 if force_all_finite: --> 645 _assert_all_finite(array, 646 allow_nan=force_all_finite == 'allow-nan') 647 ~\anaconda3\lib\site-packages\sklearn\utils\validation.py in _assert_all_finite(X, allow_nan, msg_dtype) 95 not allow_nan and not np.isfinite(X).all()): 96 type_err = 'infinity' if allow_nan else 'NaN, infinity' ---> 97 raise ValueError( 98 msg_err.format 99 (type_err, ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

Follow this topic

Posted on:

16 Feb 2021

PD model estimation - PD model estimation

0 answers ( 0 marked as helpful)