Overview
General Ideas
- Learning from Data
- Identify Patterns
- Create Models
- Identifying features
- Predicting Targets
Models
- Learned functions that maps features to predictions
- Built (trained) by analyzing patterns in training data and adjusting internal parameters to minimize error
- Can be used on new, unseen data to make predictions
Core components of a model
- Parameters - e.g., weights in linear regression or or split points in decision trees
- Features (inputs) - variables the model uses to make predictions (e.g., location to predict house prices
- Outputs (targets/predictions) - result the model produces (e.g., predicted house price)
Types of machine learning models
- Supervised learning models
- Linear Regression
- Logistic Regression
- Tree-based models
- Support Vector Machines (SVMs)
- Neural Networks
- Unsupervised learning models
- Clustering models
- Dimensionality reduction models
- Reinforcement learning models
- Q-learning
Regression
Predicting a real number value
Example: Estimating Appartment Prices
Target: Price
Features: ?
Classification
Predicting a label
Example: Kind of Iris Plant
Target: Kind, eg. setosa, virginica or versicolor
Features: ?
Features and Targets
All entries must be numbers, i.e. all feature and target values must be transformed into numbers
Each feature vector is one instance/object