Metadata-Version: 2.1
Name: ML-Classification-model-selector-Basavaraj100
Version: 0.0.3
Summary: It select best classfication model
Home-page: https://github.com/Basavaraj100/ML_Classification_model_selector
Author: ML_Classification_model_selector
Author-email: benkibijali@gmail.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/Basavaraj100/ML_Classification_model_selector/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE

# ML_Classification_model_selector





## Functions available
a) Model_Selector --> it is a class
- You can import his  by 'from select_classifier.select_model import Model_Selector'
- initialise the Model_Selector
- eg: v=Model_Selector()

b) model_performances
- Here you need to provide dataframe and the name of the target column
- Dataframe should be free of missing values and categorical features
- This method results the performance of mentioned models in terms of accuracy,precision,recall,f1_score and roc_auc_score
- eg: v.model_performances(dataframe,'target')

c)select_best_model
- This method results the name of the model which performs best in mentioned metrics
- eg: v.model_performances(based_on='accuracy')

d)plot_model_performance
- This method results the line plot of performances of all models
- eg: v.plot_model_performance()



