Classifying with Confidence
Learn to predict categorical outcomes using two powerful machine learning algorithms: Logistic Regression and Decision Trees. You'll understand how they work, when to use them, and how to evaluate their performance.
What you'll achieve
Explain how Logistic Regression uses probability to classify data points.
Identify the strengths and limitations of Logistic Regression for classification tasks.
Describe the fundamental decision-making process of a Decision Tree.
Understand the concept of overfitting in Decision Trees and methods to mitigate it.
Differentiate between key classification evaluation metrics like accuracy, precision, recall, and F1-score.
Interpret a Confusion Matrix and its components.
Choose an appropriate classification algorithm (Logistic Regression vs. Decision Tree) for a given problem scenario.
Explain the importance of ROC AUC for evaluating classifier performance across different thresholds.