Parametric vs Non-Parametric Machine Learning Models
In this video, Ada discusses the differences between parametric and non-parametric machine learning models. Parametric models have a fixed number of parameters and their complexity doesn't increase with the training dataset size. Non-parametric models adapt to the complexity of the data and can handle feature engineering effectively. Parametric models include linear regression, logistic regression, and neural networks, while non-parametric models include decision trees, random forests, and gradient boosting. Understanding these differences helps in selecting the appropriate model for specific data and problems.
Tutorial: https://sefiks.com/2020/05/02/paramet...
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