return the epsilon satisfactory instance of x.

return path index list containing [{leaf node id, inequality symbol, threshold, feature index}]. estimator: decision tree maxj: the numbe

Set up matplotlib's RC params for LaTeX plotting. Call this before plotting a figure. Parameters ---------- fig_width : float, optional, inch

Add intercept to the data before linear classification

This function return the active feature tweaking vector. x: feature vector class_labels: list containing the all class labels counter

input: 1-D arr with int vals -- if not int vals, will raise an error output: m (ndarray): one-hot encoded matrix d (d

if load_data_size is set to None (or if no argument is provided), then we load and return the whole data if it is a number, say 10000