When machine learning models show exceptional accuracy on training data sets, but perform poorly on new, unseen data, they are guilty of overfitting. Overfitting happens when models “learn” from noise in data instead of from true signal patterns.
Detecting overfitting is the first step. Comparing accuracy against a portion of training that was data set aside for testing will reveal when models are overfitting. Techniques to minimize overfitting include:
Squark Seer automatically employs these and other approaches to minimize overfitting. As always, get in touch if you have questions about Overfitting or any other Machine Learning topic. We’re happy to help.
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