[scikit-learn] Differences between scikit-learn and Spark.ml for regression toy problem

(this was also posted to stackoverflow on 03/10) I am setting up a very simple logistic regression problem in scikit-learn and in spark.ml, and the results diverge: the models they learn are different, but I can't figure out why (data is the same, model type is the same, regularization is the same

Re: [scikit-learn] Label encoding for classifiers and soft targets

> On 12 Mar 2017, at 18:38, Gael Varoquaux > wrote: > > You can use sample weights to go a bit in this direction. But in general, > the mathematical meaning of your intuitions will depend on the > classifier, so they will not be general ways of implementing them without > a lot of tinkering. I

Re: [scikit-learn] Label encoding for classifiers and soft targets

> Would it be simple to modify sklearn code to do this, or would it require a > lot of tinkering > such as modifying every single classifier under the sun? You can use sample weights to go a bit in this direction. But in general, the mathematical meaning of your intuitions will depend on the cl

Re: [scikit-learn] Error while using GridSearchCV.

Hi, guys! Thanks for the responses. @Fernando: Yes, this code is, in fact, part of Udacity's Boston Housing project. I'm currently working on my MLE Nanodegree. I was able to modify the code to go with *sklearn.model_selection*, as you suggested. And, it's great to see you help Udacity students