Machine Learning: Regression with GraphLab Create
| bias-variance trade-off | gradient descent | |||
| Ridge regression | cross-validation | measure of fit + measure of model complexity | ||
| Lasso regression | coordinate descent | feature selection | measure of fit + (different) measure of model complexity | |
| Nearest Neighbor Regression & Kernel Regression | ||||
| concave | hax Max value | |||
| convex | has Min value | |||
| Models
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• Linear regression • Regularization: Ridge (L2), Lasso (L1) • Nearest neighbor and kernel regression |
| Algorithms | • Gradient descent • Coordinate descent |
| Concepts
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• Loss functions, bias-variance tradeoff,
cross-validation, sparsity, overfitting, model selection, feature selection |