Out of curiosity, how does supervised PCA compare to LDA (Linear Discriminant
Analysis); in a nutshell, what would be the main difference?
Best,
Sebastian
> On Jul 29, 2015, at 5:41 PM, Stylianos Kampakis
> wrote:
>
> Hi Andreas,
>
> Sure. Actually, the purpose of the model is both regulariz
Indeed it sounds interesting but I'd still be curious as to how it
compares against elasticnet.
On 07/29/2015 05:41 PM, Stylianos Kampakis wrote:
Hi Andreas,
Sure. Actually, the purpose of the model is both regularization and
dimensionality reduction for problems where the number of features
Hi Andreas,
Sure. Actually, the purpose of the model is both regularization and
dimensionality reduction for problems where the number of features can be
larger than the number of instances (or in any case when there is a large
number of features). It is particularly effective when there are lots
Hm, I'm not entirely sure how score_samples is currently used, but I
think it is the probability
under a density model.
It would "only" change the meaning in so far as it is a conditional
distribution over y given x and not x.
I'm not totally opposed to adding a new method, though I'm not sure I
I am not sure about the name, score_samples would sound a bit strange
for a conditional probability in my opinion. And likelihood is also
misleading since its actually a conditional probability and not a
conditional likelihood (the quantities on the right-hand side of
conditioning are fixed and
Shouldn't that be "score_samples"?
Well, it is a conditional likelihood p(y|x), not p(x) or p(x, y).
But it is the likelihood of some data given the model.
On 07/29/2015 02:58 AM, Jan Hendrik Metzen wrote:
> Such a predict_proba_at() method would also make sense for Gaussian
> process regression.
