K-Nearest Neighbors
Published:
KNN classifier and regressor: a non-parametric approach to classification and regression
Study
Personal study notes from An Introduction to Statistical Learning (ISL) with Python implementations.
Chapter 2: Statistical Learning
Assessing Model Accuracy
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Measuring the quality of fit: MSE, bias-variance trade-off, and training vs. test error
Basics of Statistical Learning
Published:
Introduction to statistical learning concepts with Python (NumPy, Pandas, Matplotlib)
Chapter 3: Linear Regression
Gradient Descent
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Optimization algorithm for minimizing the cost function in linear regression
Multiple Linear Regression
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Extending linear regression to multiple predictors, interaction terms, and polynomial regression
Simple Linear Regression
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Fitting a linear model with a single predictor using OLS
Chapter 4: Classification
Generalized Linear Models
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Extending linear models beyond Gaussian: Poisson regression and the GLM framework
Generative Models for Classification
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LDA, QDA, and Naive Bayes classifiers based on Bayes theorem
Logistic Regression
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Binary classification using the logistic function and maximum likelihood estimation
Chapter 5: Resampling Methods
The Bootstrap
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Estimating uncertainty and standard errors through resampling
Cross-Validation
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Model assessment and selection using validation set, LOOCV, and k-fold cross-validation
Chapter 6: Linear Model Selection & Regularization
Subset Selection
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Best subset, forward stepwise, and backward stepwise selection methods
Ridge and Lasso Regression
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Shrinkage methods for regularization: L1 and L2 penalties
Chapter 7: Moving Beyond Linearity
Polynomial Regression and Splines
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Non-linear extensions of linear models: polynomial, step functions, and regression splines
Chapter 8: Tree-Based Methods
Ensemble Learning
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Boosting and other ensemble strategies for improved prediction
Bagging and Random Forests
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Ensemble methods using bootstrap aggregation and random feature selection
Decision Trees
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Recursive binary splitting for classification and regression trees
Dimension Reduction
Principal Component Analysis (PCA)
Published:
Dimensionality reduction using PCA: theory, implementation, and visualization