CODEWITHSUNDEEP
azureclassificationpredictionconfusion-matrixazure-machine-learning-service
CasellaJr
CasellaJr

Reputation: 458

Evaluate Model Node in Azure ML Studio does not take all the rows of the dataset in confusion matrix

I have this dataset in which the positive class consists of component failures for a specific component of the APS system. I am doing Predictive Maintenance using Microsoft Azure Machine Learning Studio. As you can see from the pictures below, I am using 4 algorithm: Logistic Regression, Random Forest, Decision Tree and SVM. And you can see that the Output dataset in the score model node consists of 16k rows. However, when I see the output of the Evaluate Model, in the confusion matrix there are only 160 observations for the Logistic Regression, and the correct number, 16k for Random Forest. I have the same problem, only 160 observations in the models of Decision Tree and SVM. And the same problem is repeated in other experiments for example after feature selection, normalization etc.: some evaluate model does not use all the rows of the test dataset, and some other node does it. How can I fix this problem? Because I am interested in the real number of false positive and false negatives. Experiments 16k rows wrong confusion matrix correct confusion matrix

Upvotes: 1

Views: 283

Answers (1)

Ram
Ram

Reputation: 2754

The output metrics shown are based on the validation set (e.g. “validation metric”, “val-accuracy”).All the metrics computed and displayed are on validation set and not on the original training set. All those metrics are calculated only over the validation set without considering the training set, otherwise we would inflate the performances of the model by considering data already used to train the model.

Upvotes: 0

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