While we keep working on the docs and figures, here is a little example you
all can already run:
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn
Docs are coming soon. In the meantime
, Imagine a first step containing a TrainTestSplit class with a similar
behaviour to train_test_split but capable of producing results by using fit
and predict (this is a goodie). The inputs will be X, y, z, ... , and the
outputs the same names + _train and _t
Very cool! Thanks for all the great work.
Andrew
<~~~>
J. Andrew Howe, PhD
www.andrewhowe.com
http://orcid.org/-0002-3553-1990
http://www.linkedin.com/in/ahowe42
https://www.researchgate.net/profile/John_Howe12/
I live to learn, so I can learn to live. - me
<~
Thanks Manuel, that looks pretty cool.
Do you have a write-up about it? I don't entirely understand the
connections setup.
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cool! We have been talking for a while about how to pass other things
around grid search and other meta-analysis estimators. This injection
approach looks pretty neat as a way to express it. Will need to mull on it.
On 8 Feb 2018 2:51 am, "Manuel Castejón Limas"
wrote:
> Dear all,
>
> after some
