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Master advanced ML/DS statistics
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Module 6
Classification Models and Evaluation
1
Logistic Regression for Binary Classification
Model binary classification probabilities using the logistic (sigmoid) function.
2
Interpreting Logistic Regression Coefficients
Interpret logistic regression coefficients in terms of log-odds ratios.
3
Multi-Class Logistic Regression with Softmax
Extend logistic regression to multi-class problems using the softmax function.
4
Evaluating Classifier Performance
Evaluate a classifier's performance using a confusion matrix, precision, recall, and F1-score.
5
Understanding ROC Curves and AUC
Construct and interpret a Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC).
6
SVMs: Intuition and Margin Maximization
Explain the intuition behind Support Vector Machines (SVMs) and the concept of margin maximization.
7
Addressing Class Imbalance
Diagnose class imbalance and apply resampling or class weight techniques to address it.
8
Optimizing Classification Thresholds for Business Goals
Select an appropriate classification threshold based on business objectives.
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Linear Regression and Regularization
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Tree-Based Models and Ensembles