Metadata-Version: 1.0
Name: mlplot
Version: 0.0.0
Summary: UNKNOWN
Home-page: UNKNOWN
Author: UNKNOWN
Author-email: UNKNOWN
License: MIT License
Description: [![CircleCI](https://circleci.com/gh/sbarton272/mlplot.svg?style=svg)](https://circleci.com/gh/sbarton272/mlplot)
        [![Documentation Status](https://readthedocs.org/projects/mlplot/badge/?version=latest)](https://mlplot.readthedocs.io/en/latest/?badge=latest)
        
        # mlplot
        
        Machine learning evaluation plots using [matplotlib](https://matplotlib.org/) and [sklearn](http://scikit-learn.org/). [Check out the docs.](https://mlplot.readthedocs.io/)
        
        ## Install
        
        ```
        pip install mlplot
        ```
        
        ML Plot is runs with python 2.7, 3.5 and 3.6!
        
        ## Contributing
        
        Create a PR!
        
        # Plots
        
        Starting from [sklearn](http://scikit-learn.org/stable/modules/model_evaluation.html).
        
        ## Classification
        
        ### ROC with AUC number
        ![ROC plot](https://raw.githubusercontent.com/sbarton272/mlplot/master/mlplot/test/output/test_roc.png)
        
        ### Calibration
        ![calibration plot](https://raw.githubusercontent.com/sbarton272/mlplot/master/mlplot/test/output/test_calibration.png)
        
        ### Precision-Recall
        ![precision recall curve plot](https://raw.githubusercontent.com/sbarton272/mlplot/master/mlplot/test/output/test_precision_recall.png)
        ![precision recall threshold plot](https://raw.githubusercontent.com/sbarton272/mlplot/master/mlplot/test/output/test_precision_recall_threshold.png)
        
        ### Population histograms
        ![precision recall curve plot](https://raw.githubusercontent.com/sbarton272/mlplot/master/mlplot/test/output/test_precision_recall.png)
        
        ### TODO
        - Residual plot --> what is the real name?
        - Confusion matrix
        - Full report
          - Accuracy score
          - F1 score
          - Number of samples of each class
        
        ## Regression
        
        - Full report
          - Mean sqr error
          - Mean abs error
          - Target mean, std
          - R2
        - Residual plot
        - Scatter plot
        - Histogram of regressor
        
        ## Library Cleanup
        
        - Validate y_true and y_pred
        - Add optional ax input
        - Try in a notebook
        - Return value standardization
        - Matplotlib test suite
        
Platform: UNKNOWN
