On Mon, Oct 5, 2015 at 10:05 PM, Sturla Molden
wrote:
> On 06/10/15 00:35, josef.p...@gmail.com wrote:
>
> > rate in the sense of proportion is between zero and 1.
>
> Rate usually refers to "events per unit of time or exposure", so we can
> either count events in intervals or record time-stamps
On 06/10/15 00:35, josef.p...@gmail.com wrote:
> rate in the sense of proportion is between zero and 1.
Rate usually refers to "events per unit of time or exposure", so we can
either count events in intervals or record time-stamps as our dependent
variable. If the stochastic counting process is
On Mon, Oct 5, 2015 at 6:15 PM, Sturla Molden
wrote:
> On 04/10/15 05:07, George Bezerra wrote:
>
> > I am trying to follow this paper:
> >
> http://research.microsoft.com/en-us/um/people/mattri/papers/www2007/predictingclicks.pdf
> > (check out section 6.2). They use logistic regression as a reg
On 04/10/15 05:07, George Bezerra wrote:
> I am trying to follow this paper:
> http://research.microsoft.com/en-us/um/people/mattri/papers/www2007/predictingclicks.pdf
> (check out section 6.2). They use logistic regression as a regression
> model to predict the click through rate (which is contin
On 10/03/2015 11:11 PM, Michael Eickenberg wrote:
> Hi George,
>
> completely agreed that np.unique on continuous targets is messy - I
> have run into the same problem.
>
It's fixed here:
https://github.com/scikit-learn/scikit-learn/pull/5084
I've seen logistic regression used in a regression setting in a few papers
as well. A nice thing is that the predictions are mapped to [0, 1].
The correct way to add this to scikit-learn would be to add a regression
class `LogisticRegressor` and rename the existing class to
`LogisticClassifier`. T
Hi George,
completely agreed that np.unique on continuous targets is messy - I have
run into the same problem.
If I remember correctly, you can work around this by using sample_weight to
inject the continuous target into the cross entropy loss:
If p_i are the targets, then duplicate each sample,
On Sat, Oct 3, 2015 at 11:54 PM, George Bezerra wrote:
> Thanks a lot Josef. I guess it is possible to do what I wanted, though
> maybe not in scikit. Does the statsmodels version allow l1 or l2
> regularization? I'm planning to use a lot of features and let the model
> decide what is good.
>
>
s
Thanks a lot Josef. I guess it is possible to do what I wanted, though
maybe not in scikit. Does the statsmodels version allow l1 or l2
regularization? I'm planning to use a lot of features and let the model
decide what is good.
Thanks again.
On Sat, Oct 3, 2015 at 11:20 PM, wrote:
> Just to co
Just to come in here as an econometrician and statsmodels maintainer.
statsmodels intentionally doesn't enforce binary data for Logit or similar
models, any data between 0 and 1 is fine.
Logistic Regression/Logit or similar Binomial/Bernoulli models can
consistently estimate the expected value (p
*I meant section 5.
On Sat, Oct 3, 2015 at 11:07 PM, George Bezerra wrote:
> Thanks Sebastian.
>
> I am trying to follow this paper:
> http://research.microsoft.com/en-us/um/people/mattri/papers/www2007/predictingclicks.pdf
> (check out section 6.2). They use logistic regression as a regression
Thanks Sebastian.
I am trying to follow this paper:
http://research.microsoft.com/en-us/um/people/mattri/papers/www2007/predictingclicks.pdf
(check out section 6.2). They use logistic regression as a regression model
to predict the click through rate (which is continuous).
A linear regression mod
Hi, George,
logistic regression is a binary classifier by nature (class labels 0 and 1).
Scikit-learn supports multi-class classification via One-vs-One or One-vs-All
though; and there is a generalization (softmax) that gives you meaningful
probabilities for multiple classes (i.e., class probabi
Hi there,
I would like to train a logistic regression model on a continuous (i.e.,
not categorical) target variable. The target is a probability, which is why
I am using a logistic regression for this problem. However, the sklearn
function tries to find the class labels by running a unique() on th
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