Thanks, for the reply!
It looks like a high-level API similar to Sklearn pipelines.
In my opinion, for the first steps easier to add simple assess to gain the
ability to run a simple model or simple preprocessor from python.
According to your example:
Here is raw dataset, already inside this cluster cache "myName", with Label
column "MyLable".
I want to run from notebook UI imputer and knn using python API. Export results
to file storage as an example.
In my opinion, the ability to create such a simple workflow should be our goal
for the first time.
Thank You!
Best regards,
Andrei Gavrilov.
Sent with ProtonMail Secure Email.
‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
On Wednesday, March 4, 2020 10:49 PM, kencottrell <ken.cottr...@gridgain.com>
wrote:
> Andrei,
>
> I am also working with Apache Ignite ML and am interested in providing
> wrappers for Ignite ML API, but am wondering if instead of simply recreating
> the low level Java API for ML inside Python, how about creating some higher
> level services "Auto ML" workflow ? For example:
>
> 1. here is raw dataset, already inside this cluster cache "myName", with
> Label column "MyLable" , take N samples tell me which appear to be
> numeric,
> unique id, and categorical values?
>
> 2. based on N samples, , please run some analysis and tell me the top 5
> feature columns in terms of predictive value using algorithm =
> RandonForest
>
> 3. do a batch run, sample size = N, using these preprocessing steps list
> {impute, scale, etc} and algorithms (knn, Decision Tree, etc} and give me
> a
> report of accuracies obtain with each.
>
> In other words, we have a simple sample in the Tutorial demo where these
> all run and then we compare outputs - why not automate these with a Python
> Notebook UI of some sort?
>
> --
> Sent from: http://apache-ignite-developers.2346864.n4.nabble.com/
>
