Getting Scikit-Learn To Run On Top Of Pandas

[LINK:http://scikit-learn.org/stable/] Scikit-Learn is the de-facto standard Python library for general-purpose machine learning. It operates over [LINK:http://www.numpy.org/] NumPy , an efficient, but low-level, homogeneic array library. [LINK:http://pandas.pydata.org/] Pandas adds to NumPy metadata, heterogeneity, and higher-leve munging capabilities. In the field of visualization, newer generation libraries, e.g., [LINK:http://seaborn.pydata.org/] Seaborn and [LINK:http://bokeh.pydata.org/en/0.10.0/docs/user_guide/charts.html] Bokeh , are providing safer, more readable, and higher-level functionality, by operating over Pandas data structures. Some of these are implemented using [LINK:https://matplotlib.org/] Matplotlib , a lower-level NumPy-based plotting library. This talk describes a [LINK:http://ibex.readthedocs.io/en/latest/] library for a Pandas-based version of sickit-learn . Here, too, giving a Pandas interface to a machine-learning library, provides code which is safer to use, more readable, and allows direct integration with Pandas's higher-level munging capabilities. Due to the large-scale, and evolving nature, of sicikit-learn's codebase, it is infeasible to manually wrap it. Except for a small number of intentional deviations from sickit-learn, the library wraps Scikit-Learn modules lazily through module and class introspection, and dynamic module loading. Following a short review of the relevant points of Pandas and Scikit-Learn, the talk is roughly divided into two aspects: Scikit-Learn And Pandas User Perspective Safety Advantages Of Pandas-Based Estimators Using Metadata For Inter-Instance Aggregated Features And Cross-Validation Using Metadata For Advanced Meta-Algorithms: Stacking, Nested Labeled And Stratified Cross-Valdiation Python Develop Perspective Unique Challenges Of Scikit-Learn Introspection And Decoration Two Approaches For Wrapping Scikit-Learn Estimators Lazy Dynamic Module Loading