[Scikit-learn-general] Import error for Robust scaler

[Sumedh Arani]

Dear developers,

In my due process to correct am way bug posted in the issues section in
github, I tried to work on robust scaler. I tried importing it several
times but to no avail. I even tried running plot_robust_scaling.py on my
system which runs on osX which still gave me an import error. When I went
and checked in data.py file which comes in sklearn.preprocessing, the class
and the method both exist. I tried several times and several round about to
achieve the same but still end up getting inconclusive results. This in
turn prevents me from solving a bug which I proactively decided to work
upon.

Please help me figure out the same.

Thank you.

Yours sincerely,
Sumedh Arani,
PES University.
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[Scikit-learn-general] New to Contributing

[Kshitij Saraogi]

Hello,

I am Kshitij Saraogi, a second year undergraduate at IIT Kharagpur.
Machine Learning has always fascinated me and I find scikit-learn a really
interesting project.

I would like to know how can I contribute to it.
While going through the issues, I found that almost all the "Easy" issues
are under development.
So, I would really appreciate if someone can help me get started.

Thanks,
Kshitij Saraogi
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Re: [Scikit-learn-general] New to Contributing

[Raghav R V]

Hi Kshitij Sarogi,

Try easy issues with a "Need Contributor" tag.

(This link should get you there -
https://github.com/scikit-learn/scikit-learn/issues?utf8=%E2%9C%93=is%3Aopen+label%3A%22Need+Contributor%22+label%3A%22Easy%22
)

On Sat, Nov 28, 2015 at 1:42 PM, Kshitij Saraogi 
wrote:

> Hello,
>
> I am Kshitij Saraogi, a second year undergraduate at IIT Kharagpur.
> Machine Learning has always fascinated me and I find scikit-learn a really
> interesting project.
>
> I would like to know how can I contribute to it.
> While going through the issues, I found that almost all the "Easy" issues
> are under development.
> So, I would really appreciate if someone can help me get started.
>
> Thanks,
> Kshitij Saraogi
>
>
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Re: [Scikit-learn-general] classification metrics understanding

[Joel Nothman]

If you are treating your Logistic Regression output as binary (i.e. not
using predict_proba or decision_function), could you please provide the
confusion matrix?

On 26 November 2015 at 05:06, Herbert Schulz  wrote:

> Hi, i think i have some "missunderstanding" due to the classification
> metric in scikit-learn
>
>
>
> i have a 2 class problem it is 1.0 or  2.0
>
>
>  precisionrecall  f1-score   support
>
> 1.0   0.86  0.76  0.81   254
> 2.0   0.49  0.65  0.5691
>
> avg / total   0.76  0.73  0.74   345
>
>
> Specificity: [ 1.*  0.35164835*  0.]
> recall,tpr,sensitivity  [ 0. * 0.24015748*  1.]
>
>
> # this part is manually computed  ( precision, sens, spec, ballanced
> accuracy )
>
> logistic regression 0.86,* 0.76, 0.65,* 0.7
>
>
>
> The   part with:
>
> Specificity: [ 1.  0.35164835  0.]
> recall,tpr,sensitivity  [ 0.  0.24015748  1.]
>
> are computed with
>
> fpr, tpr, thresholds = metrics.roc_curve(expected, predi,
> pos_label=1)
> print "Specificity:", 1-fpr
> print "recall,tpr,sensitivity",tpr
>
> Why is th speceficity for 1-fpr  are computed wtih [ 1.
> 0.35164835  0.]
>
> and not 0.65 ?
>
> Same with recall
>
>
>
>
>
>
>
>
>
>
>
> --
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