Hi guys,
thanks a lot for all your interesting replies.
i) How can I get threshold value which the classifier has decided to take
the decision for a particular sample to be in 0 or 1 class in binary
classification using scikit? The whole purpose of my previous questions
were to know about that threshold value (either by visualising it or just
get values)?
ii) Also can I know the threshold values in each iteration which scikit
used to generate each point on ROC curve using these below lines:
fpr = dict()tpr = dict()roc_auc = dict()for i in range(n_classes):
fpr[i], tpr[i], _ = roc_curve
<http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve>(y_test[:,
i], y_score[:, i])
roc_auc[i] = auc
<http://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html#sklearn.metrics.auc>(fpr[i],
tpr[i])
# Compute micro-average ROC curve and ROC areafpr["micro"],
tpr["micro"], _ = roc_curve
<http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve>(y_test.ravel(),
y_score.ravel())roc_auc["micro"] = auc
<http://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html#sklearn.metrics.auc>(fpr["micro"],
tpr["micro"])
I'm just using this default code, but I would like to know about the
threshold it uses to generate each point of ROC
thanks!
Shalu
On Fri, Feb 20, 2015 at 5:27 PM, shalu jhanwar <shalu.jhanwa...@gmail.com>
wrote:
> Hi Sebastian,
>
> Thanks a lot for your reply. Here in the examples, only 2 features are
> used to generate these plots.
>
> i) Can I do it with more features (I have 16 features)?
>
> ii) I wanna see the decision boundary of my training and testing dataset
> to see if the model is fine or it's overfitted on my data in case of both
> Random Forest and SVM.
>
> iii) What would be the best way to know whether the model is fine or
> overfitted according to your experience?
>
> Many thanks!
> Shalu
>
> On Fri, Feb 20, 2015 at 5:07 PM, Sebastian Raschka <se.rasc...@gmail.com>
> wrote:
>
>> Hi, Shalu,
>>
>> One example for plotting decision regions would be here:
>> http://scikit-learn.org/stable/auto_examples/plot_classifier_comparison.html
>> It's basically a "brute force" approach: You define 2D grid of points and
>> then classifier each of those points. Also, the downside is that you can
>> only do that in 2D/3D.
>>
>> Best,
>> Sebastian
>>
>> > On Feb 20, 2015, at 8:29 AM, shalu jhanwar <shalu.jhanwa...@gmail.com>
>> wrote:
>> >
>> > Hi guys,
>> >
>> > I am using SVM and Random forest classifiers from scikit learn. I
>> wonder is it possible to plot the decision boundary of the model on my own
>> training dataset so that I can have a feeling of the data? Is there any
>> in-built example available in Scikit which I can refer to view " let's say
>> margins and decision boundary" in SVM in my own data after selecting best
>> model?
>> >
>> > I'd appreciate any suggestions.
>> >
>> > Thanks!
>> > Shalu
>> >
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