Metadata-Version: 2.1
Name: dabl
Version: 0.2.0
Summary: Data Analysis Baseline Library
Home-page: https://github.com/amueller/dabl
Author: Andreas Mueller
Author-email: t3kcit+githubspam@gmail.com
License: UNKNOWN
Description: # dabl
        The data analysis baseline library.
        
        - "Mr Sanchez, are you a data scientist?"
        - "I dabl, Mr president."
        
        Find more information on the [website](https://dabl.github.io/).
        
        ## Try it out
        
        ```
        pip install dabl
        ```
        
        or [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dabl/dabl/master)
        
        ## Current scope and upcoming features
        This library is very much still under development. Current code focuses mostly on exploratory visualization and preprocessing.
        There are also drop-in replacements for GridSearchCV and RandomizedSearchCV using successive halfing.
        There are preliminary portfolios in the style of
        [POSH
        auto-sklearn](https://ml.informatik.uni-freiburg.de/papers/18-AUTOML-AutoChallenge.pdf)
        to find strong models quickly.  In essence that boils down to a quick search
        over different gradient boosting models and other tree ensembles and
        potentially kernel methods.
        
        Check out the [the website](https://dabl.github.io/dev/) and [example gallery](https://dabl.github.io/0.1.9/auto_examples/index.html) to get an idea of the visualizations that are available.
        
        Stay Tuned!
        
        ## Related packages
        
        ### Pandas Profiling
        [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling) can
        provide a thorough summary of the data in only a single line of code. Using the
        ```ProfileReport()``` method, you are able to access a HTML report of your data
        that can help you find correlations and identify missing data.
        
        `dabl` focuses less on statistical measures of individual columns, and more on
        providing a quick overview via visualizations, as well as convienient
        preprocessing and model search for machine learning.
        
Platform: UNKNOWN
Description-Content-Type: text/markdown; charset=UTF-8
