Repository: scikit-learn/scikit-learn
File: sklearn/linear_model/_logistic.py (Python)

Code before:
                (
                    "'multi_class' was deprecated in version 1.5 and will be removed in"
                    " 1.8. From then on, it will always use 'multinomial'."
                    " Leave it to its default value to avoid this warning."
                ),
                FutureWarning,
            )
        elif self.multi_class == "ovr":
            warnings.warn(
                (
                    "'multi_class' was deprecated in version 1.5 and will be removed in"
                    " 1.8. Use OneVsRestClassifier(LogisticRegression(..)) instead."
                    " Leave it to its default value to avoid this warning."
                ),
                FutureWarning,
            )
        else:
            # Set to old default value.
            multi_class = "auto"
        multi_class = _check_multi_class(multi_class, solver, len(self.classes_))

        if solver == "liblinear":
            if np.max(X) > 1e30:
                raise ValueError(
                    "Using the 'liblinear' solver while X contains a maximum "
                    "value>1e30 results in a frozen fit. Please choose another solver."
                )
            if len(self.classes_) > 2:
                warnings.warn(
                    "Using the 'liblinear' solver for multiclass classification is "
                    "deprecated. An error will be raised in 1.8. Either use another "
                    "solver which supports the multinomial loss or wrap the estimator "
                    "in a OneVsRestClassifier to keep applying a one-versus-rest "
                    "scheme.",
                    FutureWarning,
                )
            if effective_n_jobs(self.n_jobs) != 1:
                warnings.warn(
                    "'n_jobs' > 1 does not have any effect when"
                    " 'solver' is set to 'liblinear'. Got 'n_jobs'"
                    " = {}.".format(effective_n_jobs(self.n_jobs))
                )
            self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear(
                X,
                y,
                self.C,
                self.fit_intercept,
                self.intercept_scaling,
                self.class_weight,
                self.penalty,
                self.dual,

Review comment (suggestion):
```suggestion
                    "value > 1e30 results in a frozen fit. Please choose another solver "
                    "or rescale the input X."
```

Code after:
                (
                    "'multi_class' was deprecated in version 1.5 and will be removed in"
                    " 1.8. From then on, it will always use 'multinomial'."
                    " Leave it to its default value to avoid this warning."
                ),
                FutureWarning,
            )
        elif self.multi_class == "ovr":
            warnings.warn(
                (
                    "'multi_class' was deprecated in version 1.5 and will be removed in"
                    " 1.8. Use OneVsRestClassifier(LogisticRegression(..)) instead."
                    " Leave it to its default value to avoid this warning."
                ),
                FutureWarning,
            )
        else:
            # Set to old default value.
            multi_class = "auto"
        multi_class = _check_multi_class(multi_class, solver, len(self.classes_))

        if solver == "liblinear":
            if np.max(X) > 1e30:
                raise ValueError(
                    "Using the 'liblinear' solver while X contains a maximum "
                    "value > 1e30 results in a frozen fit. Please choose another "
                    "solver or rescale the input X."
                )
            if len(self.classes_) > 2:
                warnings.warn(
                    "Using the 'liblinear' solver for multiclass classification is "
                    "deprecated. An error will be raised in 1.8. Either use another "
                    "solver which supports the multinomial loss or wrap the estimator "
                    "in a OneVsRestClassifier to keep applying a one-versus-rest "
                    "scheme.",
                    FutureWarning,
                )
            if effective_n_jobs(self.n_jobs) != 1:
                warnings.warn(
                    "'n_jobs' > 1 does not have any effect when"
                    " 'solver' is set to 'liblinear'. Got 'n_jobs'"
                    " = {}.".format(effective_n_jobs(self.n_jobs))
                )
            self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear(
                X,
                y,
                self.C,
                self.fit_intercept,
                self.intercept_scaling,
                self.class_weight,
                self.penalty,