README.md

adaboost

AdaBoost

This is based on a linear regression trainer and feature selection class initially developed to help analyze and make predictions for the MIT Big Data Challenge. The trainer can use any provided solver to perform a linear regression (by default, it uses the numpy provided linear least squares regression). The training class provides a simple way to do feature selection over a large feature space. The trainer does k-fold cross validation to find features that improve validation scores. When complete, the class has the model coefficients as well as a score.

Dependencies: Python 2.7, numpy

Usage:

import adaboost

t = adaboost.AdaBoost()

# print detailed debugging information regarding the classifier selection
t.debug = 2

# train classifier
t.train(x, y) # x is a matrix, y is a actual classifications (-1 or 1)

# classify novel set of values, the sign of the return value is predicted binary class
novel_y_prime = t.apply_to_matrix(novel_x)

Methods

The following attributes are available for instances of the Trainer class.

• train(x, y) Will begin training on matrix x and classification set y, where y contains binary classification data (either 0/1 or -1/1). The system will evaluate potential weak classifiers and iteratively add the weak classifier that best minimizes the weighted error.

• apply_to_matrix(p_x) Applies the feature selected classifiers to novel values and returning a vector with the classification predicted. Each returned classification ranges from -1 to 1. The sign is the predicted class, and the absolute value is the confidence.

Attributes

The following attributes are available for instances of the Trainer class.

• debug Allows printing of information about the training process. Can be 0 (no debugging), 1 (minimal debugging) or 2 (detailed debugging). Minimal debugging prints final scores and such data, while detailed debugging prints individual classifier additions.

• max_iterations The maximum number of weak classifiers to use.

• target_error The target error for the training data set. Once the training error is less than this value, the algorithm will stop.