NDCG Scorer for Classification
I'm solving a problem of ranking classes for each unique id based on the utilization quantity. I have 6 unique classes in the training and test data. My neural net mode predicts the utilization coressponding to each class. So if there are 10000 test samples, I have 10000X6 prediction array and 10000X6 true value array. I want to validate the model performance using NDCG metrics. I'm following the kaggle notebook attached to do so : https://www.kaggle.com/davidgasquez/ndcg-scorer
In here, the author specifies that the parameters as follows:
y_true : array, shape = [n_samples]
Ground truth (true relevance labels).
y_score : array, shape = [n_samples, n_classes]
Predicted scores.
k : int
Rank.
In my case the y_true is an array of shape n_samples X n_classes and so is my y_score. I'm not understanding how y_true can be of shape n_samples, am I misunderstanding something here?
Also would the Rank be 6 in my case?
Appreciate response.
python scikit-learn classification data-science ranking
edited Nov 20 '18 at 11:41
Vivek Kumar
16.5k42156
asked Nov 15 '18 at 23:00
iprof0214iprof0214
121110
add a comment |
I'm solving a problem of ranking classes for each unique id based on the utilization quantity. I have 6 unique classes in the training and test data. My neural net mode predicts the utilization coressponding to each class. So if there are 10000 test samples, I have 10000X6 prediction array and 10000X6 true value array. I want to validate the model performance using NDCG metrics. I'm following the kaggle notebook attached to do so : https://www.kaggle.com/davidgasquez/ndcg-scorer
In here, the author specifies that the parameters as follows:
y_true : array, shape = [n_samples]
Ground truth (true relevance labels).
y_score : array, shape = [n_samples, n_classes]
Predicted scores.
k : int
Rank.
In my case the y_true is an array of shape n_samples X n_classes and so is my y_score. I'm not understanding how y_true can be of shape n_samples, am I misunderstanding something here?
Also would the Rank be 6 in my case?
Appreciate response.
python scikit-learn classification data-science ranking
edited Nov 20 '18 at 11:41
Vivek Kumar
16.5k42156
asked Nov 15 '18 at 23:00
iprof0214iprof0214
121110
add a comment |
I'm solving a problem of ranking classes for each unique id based on the utilization quantity. I have 6 unique classes in the training and test data. My neural net mode predicts the utilization coressponding to each class. So if there are 10000 test samples, I have 10000X6 prediction array and 10000X6 true value array. I want to validate the model performance using NDCG metrics. I'm following the kaggle notebook attached to do so : https://www.kaggle.com/davidgasquez/ndcg-scorer
In here, the author specifies that the parameters as follows:
y_true : array, shape = [n_samples]
Ground truth (true relevance labels).
y_score : array, shape = [n_samples, n_classes]
Predicted scores.
k : int
Rank.
In my case the y_true is an array of shape n_samples X n_classes and so is my y_score. I'm not understanding how y_true can be of shape n_samples, am I misunderstanding something here?
Also would the Rank be 6 in my case?
Appreciate response.
python scikit-learn classification data-science ranking
edited Nov 20 '18 at 11:41
Vivek Kumar
16.5k42156
asked Nov 15 '18 at 23:00
iprof0214iprof0214
121110
I'm solving a problem of ranking classes for each unique id based on the utilization quantity. I have 6 unique classes in the training and test data. My neural net mode predicts the utilization coressponding to each class. So if there are 10000 test samples, I have 10000X6 prediction array and 10000X6 true value array. I want to validate the model performance using NDCG metrics. I'm following the kaggle notebook attached to do so : https://www.kaggle.com/davidgasquez/ndcg-scorer
In here, the author specifies that the parameters as follows:
y_true : array, shape = [n_samples]
Ground truth (true relevance labels).
y_score : array, shape = [n_samples, n_classes]
Predicted scores.
k : int
Rank.
In my case the y_true is an array of shape n_samples X n_classes and so is my y_score. I'm not understanding how y_true can be of shape n_samples, am I misunderstanding something here?
Also would the Rank be 6 in my case?
Appreciate response.
python scikit-learn classification data-science ranking
python scikit-learn classification data-science ranking
edited Nov 20 '18 at 11:41
Vivek Kumar
16.5k42156
asked Nov 15 '18 at 23:00
iprof0214iprof0214
121110
edited Nov 20 '18 at 11:41
Vivek Kumar
16.5k42156
asked Nov 15 '18 at 23:00
iprof0214iprof0214
121110
edited Nov 20 '18 at 11:41
Vivek Kumar
16.5k42156
edited Nov 20 '18 at 11:41
Vivek Kumar
16.5k42156
edited Nov 20 '18 at 11:41
Vivek Kumar
16.5k42156
16.5k42156
asked Nov 15 '18 at 23:00
iprof0214iprof0214
121110
asked Nov 15 '18 at 23:00
iprof0214iprof0214
121110
asked Nov 15 '18 at 23:00
iprof0214iprof0214
121110
121110
add a comment |
add a comment |
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