In this final lesson on Model Evaluation Post-Optimization, we navigate through logistic regression, decision trees, and explore ensemble techniques to enhance accuracy. We tie it all together by comparing models post-optimization and making an informed selection to ensure reliable predictions.
Post-optimization model evaluation transcends mere accuracy comparisons. It involves analyzing performance metrics such as precision, recall, and f1-score to choose a model that truly understands the data, ensuring selection of a model that generalizes well on unseen data.
We begin by fine-tuning our Logistic Regression model:
The best parameters found are {'C': 1, 'penalty': 'l2'}, significantly enhancing our model performance. Evaluating the optimized Logistic Regression yields:
