bhattbhavesh91 / imbalance_class_sklearn Goto Github PK

ValueError: Invalid parameter ratio for estimator SMOTE(). Check the list of available parameters with `estimator.get_params().keys()
you will get this error.

replacing that with smote__sampling_strategy should work but I am getting very bad output like this:-

`c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 262, in fit
Xt, yt = self._fit(X, y, **fit_params_steps)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 220, in _fit
X, y, fitted_transformer = fit_resample_one_cached(
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\joblib\memory.py", line 355, in call
return self.func(*args, **kwargs)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 388, in fit_resample_one
X_res, y_res = sampler.fit_resample(X, y, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\base.py", line 79, in fit_resample
self.sampling_strategy = check_sampling_strategy(
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 535, in check_sampling_strategy
_sampling_strategy_float(sampling_strategy, y, sampling_type).items()
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 373, in _sampling_strategy_float
raise ValueError(
ValueError: The specified ratio required to remove samples from the minority class while trying to generate new samples. Please increase the ratio.

warnings.warn("Estimator fit failed. The score on this train-test"
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 262, in fit
Xt, yt = self._fit(X, y, **fit_params_steps)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 220, in _fit
X, y, fitted_transformer = fit_resample_one_cached(
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\joblib\memory.py", line 355, in call
return self.func(*args, **kwargs)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 388, in fit_resample_one
X_res, y_res = sampler.fit_resample(X, y, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\base.py", line 79, in fit_resample
self.sampling_strategy = check_sampling_strategy(
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 535, in check_sampling_strategy
_sampling_strategy_float(sampling_strategy, y, sampling_type).items()
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 373, in _sampling_strategy_float
raise ValueError(
ValueError: The specified ratio required to remove samples from the minority class while trying to generate new samples. Please increase the ratio.

warnings.warn("Estimator fit failed. The score on this train-test"
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 262, in fit
Xt, yt = self._fit(X, y, **fit_params_steps)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 220, in _fit
X, y, fitted_transformer = fit_resample_one_cached(
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\joblib\memory.py", line 355, in call
return self.func(*args, **kwargs)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 388, in fit_resample_one
X_res, y_res = sampler.fit_resample(X, y, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\base.py", line 79, in fit_resample
self.sampling_strategy = check_sampling_strategy(
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 535, in check_sampling_strategy
_sampling_strategy_float(sampling_strategy, y, sampling_type).items()
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 373, in _sampling_strategy_float
raise ValueError(
ValueError: The specified ratio required to remove samples from the minority class while trying to generate new samples. Please increase the ratio.

warnings.warn("Estimator fit failed. The score on this train-test"
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 262, in fit
Xt, yt = self._fit(X, y, **fit_params_steps)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 220, in _fit
X, y, fitted_transformer = fit_resample_one_cached(
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\joblib\memory.py", line 355, in call
return self.func(*args, **kwargs)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 388, in fit_resample_one
X_res, y_res = sampler.fit_resample(X, y, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\base.py", line 79, in fit_resample
self.sampling_strategy = check_sampling_strategy(
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 535, in check_sampling_strategy
_sampling_strategy_float(sampling_strategy, y, sampling_type).items()
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 373, in _sampling_strategy_float
raise ValueError(
ValueError: The specified ratio required to remove samples from the minority class while trying to generate new samples. Please increase the ratio.

warnings.warn("Estimator fit failed. The score on this train-test"
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 262, in fit
Xt, yt = self._fit(X, y, **fit_params_steps)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 220, in _fit
X, y, fitted_transformer = fit_resample_one_cached(
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\joblib\memory.py", line 355, in call
return self.func(*args, **kwargs)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 388, in fit_resample_one
X_res, y_res = sampler.fit_resample(X, y, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\base.py", line 79, in fit_resample
self.sampling_strategy = check_sampling_strategy(
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 535, in check_sampling_strategy
_sampling_strategy_float(sampling_strategy, y, sampling_type).items()
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 373, in _sampling_strategy_float
raise ValueError(
ValueError: The specified ratio required to remove samples from the minority class while trying to generate new samples. Please increase the ratio.

warnings.warn("Estimator fit failed. The score on this train-test"
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 262, in fit
Xt, yt = self._fit(X, y, **fit_params_steps)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 220, in _fit
X, y, fitted_transformer = fit_resample_one_cached(
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\joblib\memory.py", line 355, in call
return self.func(*args, **kwargs)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 388, in fit_resample_one
X_res, y_res = sampler.fit_resample(X, y, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\base.py", line 79, in fit_resample
self.sampling_strategy = check_sampling_strategy(
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 535, in check_sampling_strategy
_sampling_strategy_float(sampling_strategy, y, sampling_type).items()
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 373, in _sampling_strategy_float
raise ValueError(
ValueError: The specified ratio required to remove samples from the minority class while trying to generate new samples. Please increase the ratio.

warnings.warn("Estimator fit failed. The score on this train-test"
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 262, in fit
Xt, yt = self._fit(X, y, **fit_params_steps)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 220, in _fit
X, y, fitted_transformer = fit_resample_one_cached(
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\joblib\memory.py", line 355, in call
return self.func(*args, **kwargs)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 388, in fit_resample_one
X_res, y_res = sampler.fit_resample(X, y, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\base.py", line 79, in fit_resample
self.sampling_strategy = check_sampling_strategy(
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 535, in check_sampling_strategy
_sampling_strategy_float(sampling_strategy, y, sampling_type).items()
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 373, in _sampling_strategy_float
raise ValueError(
ValueError: The specified ratio required to remove samples from the minority class while trying to generate new samples. Please increase the ratio.

warnings.warn("Estimator fit failed. The score on this train-test"
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 262, in fit
Xt, yt = self._fit(X, y, **fit_params_steps)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 220, in _fit
X, y, fitted_transformer = fit_resample_one_cached(
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\joblib\memory.py", line 355, in call
return self.func(*args, **kwargs)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 388, in fit_resample_one
X_res, y_res = sampler.fit_resample(X, y, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\base.py", line 79, in fit_resample
self.sampling_strategy = check_sampling_strategy(
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 535, in check_sampling_strategy
_sampling_strategy_float(sampling_strategy, y, sampling_type).items()
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 373, in _sampling_strategy_float
raise ValueError(
ValueError: The specified ratio required to remove samples from the minority class while trying to generate new samples. Please increase the ratio.

warnings.warn("Estimator fit failed. The score on this train-test"
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 262, in fit
Xt, yt = self._fit(X, y, **fit_params_steps)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 220, in _fit
X, y, fitted_transformer = fit_resample_one_cached(
File "c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\joblib\memory.py", line 355, in call
return self.func(*args, **kwargs)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\pipeline.py", line 388, in fit_resample_one
X_res, y_res = sampler.fit_resample(X, y, **fit_params)
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\base.py", line 79, in fit_resample
self.sampling_strategy = check_sampling_strategy(
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 535, in check_sampling_strategy
_sampling_strategy_float(sampling_strategy, y, sampling_type).items()
File "C:\Users\BHAVYA\AppData\Roaming\Python\Python38\site-packages\imblearn\utils_validation.py", line 373, in _sampling_strategy_float
raise ValueError(
ValueError: The specified ratio required to remove samples from the minority class while trying to generate new samples. Please increase the ratio.

warnings.warn("Estimator fit failed. The score on this train-test"
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\model_selection_search.py:922: UserWarning: One or more of the test scores are non-finite: [ nan nan nan 0.1132337 0.17187154 0.18813707
0.22512345 0.28622813 0.29615575 0.31311128]
warnings.warn(
{'smote__sampling_strategy': 0.5}
c:\users\bhavya\appdata\local\programs\python\python38\lib\site-packages\sklearn\linear_model_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(`

How can I solve this ?