Assumptions Related to Distribution of Data in ML - Python Automation and Machine Learning for ICs - - An Online Book - |
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Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
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================================================================================= Asumptions related to the distribution of data in the machine learning are:
The purpose of these assumptions is to ensure that the data used for training and testing a machine learning model accurately represents the same underlying distribution. If the training and test data are not independent or drawn from different distributions, it can lead to poor generalization and unreliable model performance estimates. In practice, these assumptions are not always perfectly met, and it's important for practitioners to be aware of potential violations and take appropriate measures to mitigate them. Techniques such as cross-validation and careful data preprocessing can help address some of these concerns and ensure that machine learning models generalize well to unseen data. ============================================
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