On 03/13/2017 05:54 PM, Javier López Peña wrote:
You could use a regression model with a logistic sigmoid in the output layer.
By training a regression network with logistic activation the outputs do not
add to 1.
I just checked on a minimal example on the iris dataset.
Sorry meant softmax ;
> On 13 Mar 2017, at 21:18, Andreas Mueller wrote:
>
> No, if all the samples are normalized and your aggregation function is sane
> (like the mean), the output will also be normalised.
You are completely right, I hadn’t checked this for random forests.
Still, my purpose is to reduce model com
> You could use a regression model with a logistic sigmoid in the output layer.
By training a regression network with logistic activation the outputs do not
add to 1.
I just checked on a minimal example on the iris dataset.
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On 03/13/2017 08:35 AM, Javier López Peña wrote:
Training a regression tree would require sticking some kind of
probability normaliser at the end to ensure proper probabilities,
this might somehow hurt sharpness or calibration.
No, if all the samples are normalized and your aggregation function
On 03/12/2017 03:11 PM, Javier López Peña wrote:
The purpose is two-fold, on the one hand use the probabilities
generated by a very complex
model (e.g. a massive ensemble) to train a simpler one that achieves
comparable performance at a
fraction of the cost. Any universal classifier will do (
Hi Giles,
thanks for the suggestion!
Training a regression tree would require sticking some kind of
probability normaliser at the end to ensure proper probabilities,
this might somehow hurt sharpness or calibration.
Unfortunately, one of the things I am trying to do
with this is moving away fr
Hi Javier,
In the particular case of tree-based models, you case use the soft
labels to create a multi-output regression problem, which would yield
an equivalent classifier (one can show that reduction of variance and
the gini index would yield the same trees).
So basically,
reg = RandomForestRe
> 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
> 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
Hi there!
I have been recently experimenting with model regularization through the use of
soft targets,
and I’d like to be able to play with that from sklearn.
The main idea is as follows: imagine I want to fit a (probabilisitic)
classifier with three possible
targets, 0, 1, 2
If I pass my tr
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