Yeah ..it's here.
thanks for the explanation.
S.
On Sun, Feb 22, 2015 at 10:57 PM, Andy <t3k...@gmail.com> wrote:
> I think it is. The last character of my answer was "_".
> That is the variable name to which you assign the thresholds in your code.
>
>
> On 02/22/2015 01:54 PM, shalu jhanwar wrote:
>
> Hi Andy,
>
> thanks for the reply. I guess your second question's ans. is not
> completely displayed in the email. Could you please re-post it?
> thanks!
> S.
>
> On Sun, Feb 22, 2015 at 10:46 PM, Andy <t3k...@gmail.com> wrote:
>
>>
>> On 02/22/2015 01:21 PM, shalu jhanwar wrote:
>>
>> 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)?
>>
>> That is always 0 for decision_function.
>>
>>
>> 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
>>
>> The threshold is the third returned value of roc_curve, which you assign
>> to _
>>
>>
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