Randomly set a fraction of `p` inputs to 0 at each training update.

Recursive implementation of decision tree.

Partition a dataset into K clusters. Finds clusters by repeatedly assigning each data point to the cluster with the nearest centroid and iter

Binary logistic regression with gradient descent optimizer.

Linear regression with gradient descent optimizer.

Gaussian Naive Bayes.

Flattens multidimensional input into 2D matrix.

Gaussian Mixture Model: clusters with Gaussian prior. Finds clusters by repeatedly performing Expectation–Maximization (EM) algorithm on the

Nearest neighbors classifier. Note: if there is a tie for the most common label among the neighbors, then the predicted label is arbitrary.

Nearest neighbors regressor.

Least squares loss

Logistic loss.

Apply regular Dense layer to every timestep.

Linear combination of L1 and L2 penalties.

Gradient boosting trees with Taylor's expansion approximation (as in xgboost).

Base class for loss functions.

Vanilla RNN.

Take a specific time step from 3D tensor.