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--- ease:machinelearning:machine_learning_theory [2020/06/22 10:01] – created s_fuyedc
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 Cross-validation is a technique of training a model, where training-set and testing-set are interchanged a couple of times, to potentially exclude valleys of falsely learned influence of features.
-{{ :ease:machinelearning:xval.png}}
+{{ :ease:machinelearning:xval.png |}}
 Confusion Matrices illustrate how well the model labels the data correctly and incorrectly, given any new data. The given table takes the diagnosis of a disease as an example.
-{{ :ease:machinelearning:conf_matrix.png}}
+{{ :ease:machinelearning:conf_matrix.png |}}
 Accuracy, Precision, and Recall are measurements of the quality of a model, just like confusion matrices. If you are more interested, go over to [[https://en.wikipedia.org/wiki/Precision_and_recall|the wiki page]] about Precision and Recall.