Classification pipeline: building and evaluating

This lecture covers the process of building and evaluating a classification pipeline using tweet data sets with labeled sentiments. It explains how to aggregate annotations, create features with bag of words technique, build a pipeline with scikit-learn, evaluate classifiers using precision, recall, F1, and confusion metrics, and interpret the classifier using decision trees and feature importance. The instructor emphasizes the importance of fine-tuning parameters using cross-validation to achieve optimal performance.