Metadata-Version: 2.1
Name: slickml
Version: 0.0.5b0
Summary: SlickML is a Machine Learning Library for Python
Home-page: http://www.slickml.com
Author: Amirhessam Tahmassebi, Trace Smith
Author-email: amir.benny@gmail.com, tsmith5151@gmail.com
License: MIT
Download-URL: https://github.com/slickml/slick-ml/
Description: # SlickML: Slick Machine Learning in Python
        
        **SlickML** is a machine learning library for Python. With SlickML, you can save more time on ML-Automation and ML-Tuning. Machine learing consists of many importants tricks that can only be learned in industry while dealing with production and deployment. Thus, the main philosophy of SlickML is to bring simplicity to produce more effective models with a minimal amount of code.
        
        ## Installation
        
        First, install Python 3.6 from https://www.python.org. and then run:
        
        ```
        pip install slickml
        ```
        
        Note: in order to avoid any potential conflicts with other Python packages it's recommended to use a virtual environment, e.g. python3 virtualenv (see python3 virtualenv documentation) or conda environments for further documentaiton.
        
        
        ## Quick Start
        Here is an example using SlickML to quickly visualize the binary classification metrics based on multiple calculated thresholds:
        
        ```python
        # train a classifier using loaded train/test data
        from sklearn.ensemble import RandomForestClassifier
        clf = RandomForestClassifier()
        clf.fit(X_train, y_train)
        y_pred_proba = clf.predict_proba(X_test)[:, 1]
        
        # import slickml
        from slickml.metrics import BinaryClassificationMetrics
        example = BinaryClassificationMetrics(y_test, y_pred_proba)
        example.plot()
        
        ```
        ![slickml viz1](https://raw.githubusercontent.com/slickml/slick-ml/master/assets/images/metrics2.png)
        
        ## Contributing
        
        Please read the Contributing document to understand the requirements for submitting pull-requests. Note before 
        starting any major new feature work, please open an issue describing what you are planning to work on. This will 
        ensure that interested parties can give valuable feedback on the feature, and let others know that you are working 
        on it. 
        
        Whether the contributions consists of adding new features,  optimizing code, or assisting with the documentation, we 
        welcome new contributors of all experience levels. The SlickML community goals are to be helpful and effective
        
        ## Citing SlickML
        If you use SlickML in academic work, please consider citing https://doi.org/10.1117/12.2304418
        
        ### Bibtex Entry:
        ```bib
        @inproceedings{tahmassebi2018ideeple,
          title={ideeple: Deep learning in a flash},
          author={Tahmassebi, Amirhessam},
          booktitle={Disruptive Technologies in Information Sciences},
          volume={10652},
          pages={106520S},
          year={2018},
          organization={International Society for Optics and Photonics}
        }
        ```
        ### APA Entry:
        
        Tahmassebi, A. (2018, May). ideeple: Deep learning in a flash. In Disruptive Technologies in Information Sciences (Vol. 10652, p. 106520S). International Society for Optics and Photonics.
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Requires-Python: >=3.6
Description-Content-Type: text/markdown
