MLG 007 Logistic Regression

Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/7. See Andrew Ng Week 3 Lecture Notes Overview Logistic Function • : A sigmoid function transforming linear regression output to logits, providing a probability between 0 and 1. Binary Classification • : Logistic regression deals with binary outcomes, determining either 0 or 1 based on a threshold (e.g., 0.5). Error Function • : Uses log likelihood to measure the accuracy of predictions in logistic regression. Gradient Descent • : Optimizes the model by adjusting weights to minimize the error function. Classification vs Regression Classification • : Predicts a discrete label (e.g., a cat or dog). Regression • : Predicts a continuous outcome (e.g., house price). Practical Example • Train on a dataset of house features to predict if a house is 'expensive' based on labeled data. • Automatically categorize into 0 (not expensive) or 1 (expensive) through training and gradient descent. Logistic Regression in Machine Learning Neurons in Neural Networks • : Act as building blocks, as logistic regression is used to create neurons for more complex models like neural networks. Composable Functions • : Demonstrates the compositional nature of machine learning algorithms where functions are built on other functions (e.g., logistic built on linear).