Isolated ml engine for further developement. Added ability to test different inputs (models, features, algo kwargs...) in loop.
Cleaning+output test results to table instead of to plots. Table data will have to be sent to plots later.
Got back to it, code was unfinished and not functional, got it working for combination of model inputs and to output proper output plot, although not clean yet.
analyzeML.py: cleaning
mlUtils.py: most changes to get summary plot of all combination and RMSE vs sizeSet.
Got random forest working (and added sample code to visualize trees), and added more data (corr and success rate) to summary output table.
Split some code into several functions.
Got kwargs properly reported, and added coeffs to table output.
Clustering setup for my dataset, functional but still just scripted and results not good.
Restructured all code to object oriented. Tested and functional.
Fixes and new functionalities, made to work with new personal dataset.
 * New files output (png) for plots.
 * New stats
 * ability to set ratio of data for training vs test.
 * Fixed shuffle when using one model (tableModel).
 * Added several new sklearn models, especially for classification, not all tested.
 * made scatter plot generation generic.
Some comments and var renaming.
var renaming.
Added splitRatio to model comparison. It is better criteria than setSize, but sizeSet still supported.
Renamed clf to skModel as clf not always classifier, some instances kept when specific to classifier.
Minor fixes and code cleaning.
comments only. old updates.