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


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