CST383 Learning Log #7

 This week focused on classification models, particularly logistic regression and k-nearest neighbors (kNN), as well as evaluating model performance. One of the most useful concepts I learned was how logistic regression uses the sigmoid function to convert a linear combination of predictors into a probability between 0 and 1. Before this week, I understood classification at a high level, but I did not fully understand how a model could estimate the probability that an observation belongs to a particular class.

I also learned more about confusion matrices and the metrics derived from them, including accuracy, precision, recall, and false positives. Working through examples helped me see that accuracy alone does not always tell the full story, especially when one type of error is more important than another. Understanding recall was particularly helpful because it measures how well a model identifies actual positive cases.


Another important topic was the comparison between logistic regression and kNN. I learned that logistic regression generally requires less storage and can make predictions much faster once it has been trained, while kNN stores all training examples and performs calculations every time a prediction is needed. This helped me understand why different algorithms may be better suited for different situations.


One concept that I still find somewhat challenging is hyperparameter tuning and determining which parameter values are most appropriate for a given model. I understand the mechanics of using tools like GridSearchCV, but I would like more practice interpreting the results and understanding why certain parameter combinations perform better than others.


This week strengthened my understanding of classification problems and model evaluation. I feel much more comfortable reading confusion matrices, interpreting classification metrics, and understanding the tradeoffs between different classification algorithms. I would like to continue practicing model selection and hyperparameter tuning so that I can become more confident in choosing and improving machine learning models.

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