On 03/22/2016 03:27 AM, Gilles Louppe wrote:
> Unfortunately, the most important parameters to adjust to maximize
> accuracy are often those controlling the randomness in the algorithm,
> i.e. max_features for which this strategy is not possible.
>
> That being said, in the case of boosting, I th
Interesting,
Yes max_features is probably the most important parameter. However those
other parameters may have big contribution to reduce overfitting too.
I would probably make some test but I am not experienced with the low level
API of scikit-learn.
Any experimented scikit-learn contributors w
Unfortunately, the most important parameters to adjust to maximize
accuracy are often those controlling the randomness in the algorithm,
i.e. max_features for which this strategy is not possible.
That being said, in the case of boosting, I think this strategy would
be worth automatizing, e.g. to a
Related issue:
https://github.com/scikit-learn/scikit-learn/issues/3652
On Tue, Mar 22, 2016 at 6:32 AM, Jacob Schreiber
wrote:
> It should if you're using those parameters. It's basically similar to
> calculating the regularization path for LASSO, since these are also
> regularization terms. I
It should if you're using those parameters. It's basically similar to
calculating the regularization path for LASSO, since these are also
regularization terms. I think this would probably be a good addition if
there was a clean implementation for it.
On Mon, Mar 21, 2016 at 2:19 PM, Lam Dang wrot
Hi Jacob,
Thanks for your answer. Indeed you are right, some parameters cannot be
adjusted off-data. Let's go through the parameters list to see which one
can be adjusted:
n_estimators : this is simple - the more the better
criterion : No
max_features : No
max_depth : Yes
min_samples_split : Yes
m
Hi Lam
The idea of exploiting redundancies to speed up algorithms is a good
intuition. However, I don't think that most attributes would be able to be
done in this manner. For example, considering different numbers of max
features in the splits would be difficult to calculate without storing all
p
