Evaluating Model Generalization with Cross Validation
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Cross Validation is a widely used technique in Machine Learning to evaluate the performance of a model on unseen data. But what does it really mean when we say a model generalizes well? In this video, we delve into the concept of cross validation and how it can be used to evaluate model generalization. We'll explore the different types of cross validation techniques and discuss their applications.
One of the key challenges in Machine Learning is overfitting. A model that is too complex and is trained on a small dataset is likely to perform well on the training data but poorly on new, unseen data. Cross validation helps to identify this problem by evaluating the model's performance on multiple subsets of the data. By averaging the performances across these subsets, we can get a more accurate estimate of how well the model will generalize to new data.
Cross validation can also be used to select the best model among multiple models. By training each model on a different subset of the data and evaluating their performances, we can identify the model that performs best on unseen data. This technique is especially useful when we have multiple models that are all good fits for the training data, but we want to choose the one that will generalize best to new data.
Some suggested readings to further reinforce this topic include:
* "An Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
* "Pattern Recognition and Machine Learning" by Christopher Bishop
* "Cross-validation in machine learning" by scikit-learn documentation
#MachineLearning #CrossValidation #ModelEvaluation #DataScience #STEM #ArtificialIntelligence #DeepLearning #DataAnalysis
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