Metadata-Version: 2.1
Name: ddplt
Version: 0.0.2.dev2
Summary: Useful utility functions for evaluation of ML
Home-page: https://github.com/ielis/ddplt
Author: Daniel Danis
Author-email: daniel.gordon.danis@gmail.com
License: GPLv3
Description: # ddplt
        
        Useful utility functions for evaluation of ML.
        
        **Motivation:**
        The main motivation behind this package is to create a single place where the utility functions for ML projects are located. These functions represent the best I was able to scrape from various tutorials or offical documentation on the web.
        
        
        ## Confusion matrix
        
        This function prints and plots the confusion matrix.
        
        The code:
        
        ```python
        import numpy as np
        from ddplt import plot_confusion_matrix
        
        y_test = np.array([0, 0, 1, 1, 2, 0])
        y_pred = np.array([0, 1, 1, 2, 2, 0])
        class_names = np.array(['hip', 'hop', 'pop'])
        ax, cm = plot_confusion_matrix(y_test, y_pred, class_names)
        ```
        
        will create a plot like:
        ![conf_matrix](img/cm_hip_hop_pop.png)
        
        
        ## Learning curve
        
        Create plot showing performance evaluation for different sizes of training data. The method should accept: 
        - existing `Axes`
        - performance measure (e.g. accuracy, MSE, precision, recall, etc.)
        - ...
        
        
        ## ROC curve
        
        Plot showing Receiver Operating Characteristics of a predictor.
        
        
        ## Correlation heatmap
        
        Grid where each square has a color denoting strength of a correlation between predictors. You can choose between Pearson and Spearman correlation coefficient, the result is shown inside the square. 
        
        
Keywords: plotting machine learning evaluation metrics
Platform: UNKNOWN
Description-Content-Type: text/markdown
