Tom,
for linear regression models (Lasso et al.) there is no support but
you can achieve what you want with manual prewhitening (taking care of
feature scaling c.f. normalize parameter).
Adding native support is conceivable for the project I think.
HTH
Alex
On Tue, Feb 11, 2014 at 3:28 PM, Tho
I had a similar situation and the solution I came up with was calculating
the standard deviation of the predictions of all the individual trees.
I found that when I trained my regressor on the lower half of my data, then
used the model to predict the upper half of my data my model generally
return
https://github.com/scikit-learn/scikit-learn/pull/2848
The current state of implementation was explained in the PR comment.
On Mon, Feb 10, 2014 at 3:14 PM, Olivier Grisel wrote:
> 2014-02-08 2:25 GMT-08:00 Arnaud Joly :
> >
> > I have looked a bit at your code and it's a great start. It would b
Hello,
I have interest in weight my samples for linear learners such as
LinearRegression, LassoLarsCV, and friends.
I found an example for SVM based on per-sample C (
http://scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html),
but this isn't the kind of learner I want to use. An
It's actually from pandas.
https://sourcegraph.com/github.com/pydata/pandas/symbols/python/pandas/tseries
Without knowing anything specific about your problem, I'm guessing you
used a 'raw' data frame as input to sklearn, and it gave back some
weird bug.
http://bit.ly/1fcKxRG
On Tue, Feb 11, 2
