On 02/02/2013 09:49 PM, Noel Dawe wrote:
>
> In examples/ensemble/plot_adaboost_twoclass.py:
>
> > @@ -0,0 +1,100 @@
> > +"""
> > +==================
> > +Two-class AdaBoost
> > +==================
> > +
> > +This example fits an AdaBoosted decision stump on a non-linearly separable
> > +classification dataset composed of two "Gaussian quantiles" clusters
> > +(see :func:`sklearn.datasets.make_gaussian_quantiles`) and plots the decision
> > +boundary, class probabilities, and two-class decision scores.
>
> Does this help?
>
> |Samples with decision scores greater than zero are
> classified as B, and are otherwise classified as A.
> |
>
> —
>
So what we see in the plot is that the algorithms is certain about most 
points?
Or what is the take-away message? Or is there something qualitiative
we can see from this?