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ML breakdown: Supervised + Unsupervised + RL
Classifier comparison:
A Unified Data Infra
AI and ML Blueprint
  • Expectation-maximization (EM): assumes random components and computes for each point a probability of being generated by each component of the model. Then iteratively tweaks the parameters to maximize the likelihood of the data given those assignments. Example: Gaussian Mixture
Selecting statistical test. Source: Statistical Rethinking 2. Free Chapter 1
  • KNN: + Simple, flexible, naturally handles multiple classes. — Slow at scale, sensitive to feature scaling and irrelevant features
Lasso equation
Learning Curve example
  • Linear Discriminant Analysis (LDA): A classifier with a linear decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule. The model fits a Gaussian density to each class, assuming that all classes share the same covariance matrix.
Normal equation
  • Random Forests: each tree is built using a sample of rows (with replacement) from training set. + Less prone to overfitting
Reinforcement Learning
  • Ridge Regression regularization: imposes a penalty on the size of the coefficients
Stochastic gradient descent cost function
validation curve example