Summary: Introduction to scikit-learn and Model Evaluation
Explore scikit-learn tools for fitting binary classification models and evaluating their performance using metrics beyond accuracy. Understand confusion matrices, true and false positive rates, ROC and precision-recall curves. Gain skills to assess classifiers effectively before advancing to logistic regression.
We'll cover the following...
We'll cover the following...
In this chapter, we finished the initial exploration of the case study data by examining the response variable. Once we became confident in the completeness and correctness of the dataset, we were prepared to explore the relation between features and response and build models.
Binary classification and its evaluation metrics
We spent much of this chapter getting used to model fitting in scikit-learn at the technical, coding level, and learning about metrics we could use ...