Metadata-Version: 2.1
Name: amlr
Version: 0.3.6
Summary: amlr - Auto Machine Learning Report
Home-page: https://github.com/dbranquinho/amlr
Author: Delermando Branquinho Filho
Author-email: delermando@gmail.com
License: MIT
Description: ## `AMLR` - Auto Machine Learning Report
        
        Create a bealtifull Machine Learning Report with *`One-Line-Code`*
        
        <hr>
        
        ![](https://img.shields.io/badge/pypi-0.3.6-blue) ![](https://img.shields.io/badge/python-6.7|6.8|6.9-lightblue) ![](https://img.shields.io/badge/Licence-MIT-lightgray) ![](https://img.shields.io/badge/status-Release-darkgreen) ![](https://img.shields.io/badge/pipeline-passed-green) ![](https://img.shields.io/badge/testing-passing-green) ![](https://img.shields.io/badge/H2O-Java-brown)
        
        
        **Main Features:**
        
        - Exploratory Data Analisys
            - Dataset Configuration
                - Shape
                - Detect number of classes (Bernoulli or binary for while)
                - Automatically Duplicate Observations dropped
                - You can drop  Duplicate Observations  manually as well
                - Exclude automatically features with highest frequencies (Names, IDs, FW keys etc)
            - Regression Analysis
            - Automatic Balance Classes
            - Correlation Analysis
            - Detecting Multicollinearity with VIF
            - Residual Analisys
        - Grid - Hyperparameter optimization
        - Partial dependence plot (PDP) 
        - Individual Conditional Expectation (ICE)
        - Variable Importance by Model
        - AML - Partial Dependence
        - Ensemble - (ICE) Individual Condition Expectation
        - Correlation Heatmap by Model
        - Model Performance
            - Analytical Performance Modeling
            - Comparative Metrics Table with:
                - Overall ACC	
                - Kappa	
                - Overall 
                - RACC	
                - SOA6(Landis & Koch)	
                - SOA6(Fleiss)	
                - SOA6(Altman)	
                - SOA6(Cicchetti)	
                - SOA6(Cramer)	
                - SOA6(Matthews)	
                - TNR Macro	
                - TPR Macro	
                - FPR Macro	
                - FNR Macro	
                - PPV Macro	
                - ACC Macro	
                - F6 Macro	
                - TNR Micro	
                - FPR Micro	
                - TPR Micro	
                - FNR Micro	
                - PPV Micro	
                - F6 Micro	
                - Scott PI	
                - Gwet AC6	
                - Bennett S	
                - Kappa Standard Error	
                - Kappa 96% CI	
                - Chi-Squared	
                - Phi-Squared	
                - Cramer V	
                - Chi-Squared DF	
                - 96% CI	
                - Standard Error	
                - Response Entropy	
                - Reference Entropy	
                - Cross Entropy	
                - Joint Entropy	
                - Conditional Entropy	
                - KL Divergence	
                - Lambda B	
                - Lambda A	
                - Kappa Unbiased	
                - Overall RACCU	
                - Kappa No Prevalence	
                - Mutual Information	
                - Overall J	
                - Hamming Loss	
                - Zero-one Loss	
                - NIR	
                - P-Value	
                - Overall CEN	
                - Overall MCEN	
                - Overall MCC	
                - RR	
                - CBA	
                - AUNU	
                - AUNP	
                - RCI	
                - Pearson C	
                - CSI	
                - ARI	
                - Bangdiwala B	
                - Krippendorff 
                - Alpha
            - The Best Algorithms Table
                - Automatically chooses the best model based on the metrics above
            - Confusion Matrix for all Models
            - Feature Importance for all models
            - Save all Models into a Pickle file
        
        
        <hr>
        
        ## How to Install
        
        ```shell
        sudo apt-get install default-jre
        pip install amlr
        ```
        
        <BR>
        <hr>
        <BR>
        
        ## How to use
        
        `sintax`:
        ```python
        from amlr import amlr as rp
        import webbrowser
        
        rp = rp.report()
        rp.create_report(dataset='data/titanic-passengers.csv', target='Survived')
        
        webbrowser.open('report/index.html')
        ```
        
        ## We tested with the following Data Sets
        
        - Classic dataset on `Titanic` disaster
            - Bernoulli Distribution Target or Binary Classification
            - Download here: [Titanic](https://public.opendatasoft.com/explore/dataset/titanic-passengers/table/)
        
        
        ### Output
        
        See the output [here](https://www.thescientist.com.br/amlr/)
        
        This is an example of the test made with the Titanic Dataset;
        
        <hr>
        <BR>
        
        `enjoi!`
        
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