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Early Stopping in Machine Learning - Python Automation and Machine Learning for ICs - - An Online Book: Python Automation and Machine Learning for ICs by Yougui Liao - |
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Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= Early stopping is a method used to prevent overfitting during the training of a machine learning model. It involves monitoring the model's performance on a validation dataset and stopping the training process when the performance on this validation set begins to deteriorate or stops improving significantly. This technique is commonly used in training deep learning and other iterative models where the risk of overfitting is high. Early stopping generally works in the way below:
Early stopping acts as a form of regularization, helping to ensure that the model generalizes well to unseen data rather than just memorizing the training set. ===========================================
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