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You should first segregate the rejected samples. You can use then use string matching or something more complex (like creating embeddings and then, taking L2 distance between them) between the different field names you have and the comment for rejection. Whichever field gets the highest score, you increase the rejection count for that field. In the end, you ...


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It is computed just like in training. You take an MSE or something along these lines between the input and the output. You set a threshold for it. If new data's reconstruction error is higher than your threshold, then it is anomalous otherwise it isn't.


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I shall suggest one more popular metric for this. Davies Bouldin Score (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.davies_bouldin_score.html#sklearn.metrics.davies_bouldin_score). You can also take a look at the clustering metrics in scikit documentation (https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics).


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You can compute "Silhouette Coefficient" for your aim. Its values mean: 1: Means clusters are well apart from each other and clearly distinguished. 0: Means clusters are indifferent, or we can say that the distance between clusters is not significant. -1: Means clusters are assigned in the wrong way. Also other measures such as purity and mutual ...


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