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Machine Learning: Algorithms & Applications
·
Module 5
Logistic Regression and Classification Evaluation
1
Demystifying Logistic Regression
Explain the intuition behind logistic regression, including the sigmoid function, log-odds, and decision boundary.
2
When to Use Logistic Regression
Explain when logistic regression is an appropriate choice for a classification problem.
3
Understanding the Confusion Matrix
Define and interpret a confusion matrix and its components (TP, FP, TN, FN).
4
Evaluating Classification Models
Compute and interpret accuracy, precision, recall, and F1-score, and explain the precision-recall tradeoff.
5
ROC Curves and AUC Calculation
Plot and interpret a Receiver Operating Characteristic (ROC) curve and compute the Area Under the Curve (AUC).
6
Logistic Regression for Binary Classification
Apply logistic regression to a binary classification dataset and evaluate it using a full suite of classification metrics.
7
Multi-Class Logistic Regression: One-vs-Rest & Softmax
Explain strategies for extending logistic regression to multi-class problems, such as one-vs-rest and softmax.
Previous module
Regularized Linear Models
Next module
Foundational Classification Algorithms