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This podcast mini-episode discusses the technique of Cross Validation, which involves splitting a dataset into small partitions, training a model, and validating its predictive power. The hosts explore the importance of models, good fit, and the process of training. They highlight the significance of cross-validation in data science to avoid overfitting and improve predictive power, using examples such as predicting sales data and training a jazz music classifier. Finally, they explain the concept of cross-validation in machine learning and its usefulness for limited or new data.
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Quick takeaways
- Cross-validation is a technique used to evaluate a model's performance and prevent overfitting by randomly dividing the data into partitions and testing the model on unseen data.
- Cross-validation helps in parameter tuning and preventing overfitting by allowing the evaluation of the model's performance on different combinations of training and evaluation sets.
Deep dives
What is fitting in statistics and data science?
In statistics and data science, fitting refers to how well a model matches the data that is being analyzed or predicted. Just like a garment should fit a model perfectly in the fashion industry, a model in statistics should accurately describe and predict the behavior of the data. There are different types of models, including explanatory models that explain why the data is a certain way, and predictive models that infer future outcomes based on historical data. The quality of a fit is determined by how closely the model aligns with the actual data.
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highlights
Listen to the best highlights from the podcasts you love and dive into the full episode