Fits Naive Bayes classifier Args: X (np.ndarray): Training data of shape[n_samples, n_features] y (np.ndarra

Computes the gradient (needed if using gradient descent). Args: X (np.ndarray): Training data of shape[n_samples, n_feat

Args: X (np.ndarray): Training data of shape[n_samples, n_features] y (np.ndarray): Target values of shape[n_samples,

generator of indices to split data of given length into test and train sets. Useful for K-fold cross validation. Args: d

CART algorithm to learn split at node in tree. Minimizes mean squared error of the two classes generated. Args:

CART algorithm to learn split at node in tree. Minimizes total misclassification error. Args: data (np.ndarray):

Local linear logistic eliminates bias at boundries of domain. It uses weighted least squares, determining weights from the k

Args: X (np.ndarray): Training data of shape[n_samples, n_features] y (np.ndarray): Target values of shape[n_samples, 1]

Args: x (np.array): Training data of shape[1, n_features] Currently, only vector of single sample is supported

Args: X (np.ndarray): Training data of shape[n_samples, n_features] y (np.ndarray): Target values of shape[n_samples, 1]

Splits X and y arrays into test and train sets Args: X (np.ndarray): Training data of shape[n_samples, n_features] y (np.nda