cst383 - week 6

    This week focused on hyperparameter tuning, KNN regression, linear regression, and evaluating regression models. There was a lot to cover in these topics but interestingly enough they gave me an appreciation for what goes into creating machine learning models. From my previous perspective I think I viewed machine learning as mostly selecting an algorithm and allowing it to produce results. But in learning about these concepts like hyperparameter tuning, it showed me that there is still a significant human element involved in the process. The performance of a model can depend heavily on the choices made by the developer, and finding the right settings requires testing and careful evaluation.

    Additionally the distinction between classification and regression was interesting because while both are forms of prediction, they are designed to solve different types of problems. Regression is useful in that many real world situations involve predicting numerical values like costs, sales, demand, or trends over time. It makes sense why linear regression remains one of the foundational techniques in data science despite the development of more advanced machine learning methods.

    This week helped me realize that building a machine learning model is a lot more involved than just choosing the right algorithm. Learning about hyperparameter tuning and model evaluation showed me how much testing and refinement goes into producing reliable results. Its reinforced the idea that machine learning is a combination of mathematical techniques, data analysis, and human decision making. This has given me a much better understanding of how predictive models are developed and why they are so valuable in such varying careers fields and industries.

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