[scikit-learn] Query Regarding Model Scoring using scikit learn's joblib library
joel.nothman at gmail.com
Mon Dec 26 15:26:28 EST 2016
Your post is terminologically confusing, so I'm not sure I've understood
your problem. Where is the "different sample" used for scoring coming from?
Is it possible it is more related to the training data than the test sample?
On 27 December 2016 at 05:28, Debabrata Ghosh <mailfordebu at gmail.com> wrote:
> Dear All,
> I need some urgent guidance and help from
> you all in model scoring. What I mean by model scoring is around the
> following steps:
> 1. I have trained a Random Classifier model using scikit-learn
> (RandomForestClassifier library)
> 2. Then I have generated the True Positive and False Positive
> predictions on my test data set using predict_proba method (I have splitted
> my data into training and test samples in 80:20 ratio)
> 3. Finally, I have dumped the model into a pkl file.
> 4. Next in another instance, I have loaded the .pkl file
> 5. I have initiated job_lib.predict_proba method for predicting the
> True Positive and False positives on a different sample. I am terming this
> step as scoring whether I am predicting without retraining the model
> My question is when I generate the True Positive Rate on
> the test data set (as part of model training approach), the rate which I am
> getting is 10 – 12%. But when I do the scoring (using the steps mentioned
> above), my True Positive Rate is shooting high upto 80%. Although, I am
> happy to get a very high TPR but my question is whether getting such a high
> TPR during the scoring phase is an expected outcome? In other words,
> whether achieving a high TPR through joblib is an accepted outcome
> vis-à-vis getting the TPR on training / test data set.
> Your views on the above ask will be really helpful as I
> am very confused whether to consider scoring the model using joblib.
> Otherwise is there any other alternative to joblib, which can help me to do
> scoring without retraining the model. Please let me know as per your
> earliest convenience as am a bit pressed
> Thanks for your help in advance!
> scikit-learn mailing list
> scikit-learn at python.org
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