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A confusion matrix is a contingency table used to evaluate the predictive accuracy of a classifier. Confusion matrix is the 2x2 frequency table with counts "True positive", "True negative", "False positive", "False negative", relating classifying to a class of interest vs. else class. But in a broader sense, any frequency kxk crosstabulation "Predicted" x "Actual" classes can be called a confusion matrix, in the context of evaluation of a classifier.

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How can accuracy be greater than my precision, recall and F-Score metrics?

I have trained two models to detect gestures using ambient light and solar panels. I am now testing the two models in different light scenarios. I have a Convolutional Neural Network model that perfor …