Explore the models
Combine independent facets instead of forcing every model into a single hierarchy.
12 models
Decision Tree
A Decision Tree predicts by following a sequence of feature-threshold questions to a leaf.
Diffusion Model
A diffusion model learns to turn noise into structured data by repeatedly reversing a gradual corruption process.
Gradient Boosting
Gradient Boosting builds a sequence of small models, each correcting the current ensemble's mistakes.
K-Means
K-Means divides points into $k$ groups by repeatedly assigning each point to its nearest center and moving centers to the assigned averages.
K-Nearest Neighbors
KNN predicts from the labels of the most similar stored examples.
Linear Regression
Linear Regression predicts a number by adding weighted feature values and a baseline offset.
Logistic Regression
Logistic Regression turns a weighted sum of features into a probability for a class.
Multilayer Perceptron
An MLP stacks learned linear transformations and nonlinear activations to model complex feature interactions.
Principal Component Analysis
PCA rotates data onto new perpendicular axes ordered by how much variance they capture.
Random Forest
Random Forest averages many deliberately varied decision trees to create a more stable predictor.
Temporal Convolutional Network
A TCN uses causal, usually dilated one-dimensional convolutions to model sequences with a large and controllable history.
Transformer
A Transformer builds context-aware representations by letting positions assign attention to other positions.