Metadata-Version: 2.1
Name: fronni
Version: 0.0.6
Summary: Machine Learning model performance metrics & charts with confidence intervals, optimized with numba to be fast
Home-page: https://github.com/facebookexperimental
Author: Kaushik Mitra
Author-email: kaushik.umcp@gmail.com
License: MIT
Download-URL: https://github.com/facebookexperimental/fronni/archive/refs/tags/v_006.tar.gz
Description: 
        # fronni
        A Python library for quickly calculating & displaying machine learning model performance metrics with confidence intervals.
        
        ## How fronni works?
        
        https://medium.com/@kaushikm/fronni-a-python-library-for-quickly-calculating-machine-learning-model-performance-metrics-with-3baf28eaa5c0
        
        ## Requirements
        
        * Python >= 3.6
        
        * numba
        
        * numpy
        
        * scikit-learn
        
        * plotly
        
        ## Installing fronni
        
        pip install fronni
        
        
        ## Full documentation
        
        Functions from the classification module:
        
        ### classification_report
        
        Generates confidence intervals for precision, recall, & F1 metrics for a binary or multi-class classification model, given arrays of predicted & label values.
        
        | Parameter | Type | Default |
        |--|--|--|
        | label | Numpy array or Pandas series | None
        | predicted | Numpy array or Pandas series | None
        | n | integer, number of bootstrap iterations | 1,000
        | confidence_level | integer value between 1 & 100 | 95
        | as_dict | Boolean, return nested dictionary if True otherwise Pandas dataframe | False
        | confidence_level | value between 1 & 100 | 95
        | sort_by_sample_size | Boolean, return the Pandas dataframe, sorted in descending order of class sample size | False
        
        ### plot_classification_report
        
        Plots precision, recall, & confidence intervals for F1 metrics for a binary or multi-class classification model, given a classification report input.
        
        | Parameter | Type | Default |
        |--|--|--|
        | report | output from classification_report | None
        | save_to_filename | string, path of filename image to save like "image.png" | None
        
        From the regression module:
        
        ### regression_report
        
        Generates confidence intervals for RMSE, MAE, and R^2 metrics for a regression model, given arrays of predicted & label values.
        
        | Parameter | Type | Default |
        |--|--|--|
        | label | Numpy array or Pandas series | None
        | predicted | Numpy array or Pandas series | None
        | n | integer, number of bootstrap iterations | 1,000
        | as_dict | Boolean, return nested dictionary if True otherwise Pandas dataframe | False
        
        See the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out.
        
        ## License
        
        fronni is Apache 2.0 licensed, as found in the LICENSE file.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
