- Logistic Regression Models - classifies along with probabilities
- Support Vector Machines (SVM) - classifies without probabilities
When to Use (Linear SVM - Non-Linear SVM - Logistic Regression)
- 𝑛 - number of training examples
- 𝑘 - number of features
if 𝑘 is large (relative to 𝑛) (e.g. 𝑘≥𝑛, 𝑘=10,000, 𝑛=10-1000)
- use logistic regression or SVM without a kernel (linear kernel)
if 𝑘 is small, 𝑛 is intermediate (e.g. 𝑘=1-1000, 𝑛=10-10,000)
- use Non-Linear SVM with Gaussian kernel
if 𝑘 is small, 𝑛 is large (e.g. 𝑘=1-1000, 𝑛=50,000+)
- create/add more feature, then use logistic regression or SVM without a kernel
- bc SVM is slow when number of training examples are large
neural network likely to work well for most of these settings, but may be slower to train