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The 1176 is the number of neurons in the layer before the last layer, which is the number of pixels in the convolutional layer but flattened (height*width*n_filters, i.e. 7*7*24=1176). The 3 comes from the number of neurons in the final layer, since you have 3 classes your final layer has 3 neurons, 1 for each class (which will output zero or one). This then ...


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The ROC curve is built by taking different decision thresholds, and should be built using the predict_proba of your estimator. In particular, in your multiclass example, the ROC is using the values 0,1,2 as a rank-ordering! So there are four thresholds, the one between 0 and 1 being the most important here: there, you declare all of the samples the model ...


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When you compute the ROC, you're varying the decision threshold, while the confusion matrix and those metrics based on it are using a default threshold (probability 0.5 for the logistic regression, and the max-margin boundary of the SVM [which isn't meant to be probabilistic by itself]). So the logistic regression is doing at least something meaningful at ...


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Those scorings looks strange for me, but beside that you must remember that F1, accuracy, confusion matrix, etc depends on the chosen threshold, while AUC is threshold-independent (it is an integral over all of the thresholds from 0 to 1). Your models return some probabilities of being a member of class 1. If you choose to label by '1' only those, which ...


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I was wondering, is it a proper method to convey information via separate Recall and Precision Confusion matrix? Usual or not this could be very important. They convey different things. So in medical purposes, or generally in different context, certain recall values for certain classes could be very interesting and important because we dont wont to commit ...


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This is very unusual according to my experience, and I agree that it's difficult to interpret. There is a single value for either precision or recall for a particular label, but since these tables are presented as confusion matrices the values cannot be precision/recall. I notice that the matrices show percentages which sum to 100 across each row for the "...


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No. A confusion matrix is by definition a tabulation of real classes and predicted classes per subject. I've seen relative counts but they are not standard. According to Wikipedia: Each row of the matrix represents the instances in a predicted class while each column represents the instances in an actual class (or vice versa). The matrices listed above ...


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It's difficult to answer precisely without knowing the data and the task. Assuming it's a single column of values with no order involved, it boils down to finding the optimal threshold to separate regular values vs. anomalies. Given that you know which ones are the anomalies (labelled data), the problem can be treated as a binary classification task: a ...


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No, a confusion matrix doesn't make sense here. While you are assigning inputs to clusters, you do not know which cluster is 'correct' for each input. That is, it is not a supervised multi-class classification problem. You do not even know what each cluster "means". All you can measure here are metrics like intra-cluster distance: how far on average are ...


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Confusion matrix needs groun truth values and predicted values. You have _____, so you need other part for this multiclass confusion matrix.


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