SPIDER The Spider Objects
data - Storing input data and output results data_fast - Implementation of data object that limits memory overhead algorithm - Generic algorithm object group - Groups sets of objects together (algorithms or data) loss - Evaluates loss functions get_mean - Takes mean loss over groups of algs chain - Builds chains of objects: output of one to input of another param - To train and test different hyperparameters of an object cv - Cross validation using objects given data kernel - Evaluates and caches kernel functions distance - Evaluates and caches distance functions wilcoxon - Wilcoxon test of statistical significance of results
Core library objects. normalize - Simple normalization of data sigmoid - Sigmoid normalization
Pre-Processing objects svm - Support Vector Machine (svm) c45 - C4.5 for binary or multi-class knn - k-nearest neighbours lda - Linear Discriminant platt - Conditional Probability estimation for margin classifiers anorm - Minimize the a-norm in alpha space using kernels loom - Leave-one-out machine - kernel based classifier stab - Maximum Stability Classifier (via cv recursion) kde - Kernel Dependency Estimation: general input/output machine
Pattern Recognition objects. one_vs_rest - Voting method of one against the rest (also for multi-label) one_vs_one - Voting method of one against one mc_svm - Multi-class SVM c45 - C4.5 for binary or multi-class knn - k-nearest neighbours
Multi-Class and Multi-label objects. feat_sel - Generic object for feature selection + classifier r2w2_sel - SVM Bound-based gradient descent selection rfe - Recursive Feature Elimination (also for the non-linear case) l0 - Dual zero-norm minimization (Weston, Elisseeff) fsv - Primal zero-norm based feature selection (Mangasarian) golub - Golub's feature selection method using correlation scores mars - selection algorithm of Friedman (greedy selection) corrub - Unbalanced correlation score (used in the KDD Cup results) clustub - Multi-class feature selection using spectral clustering
Feature Selection objects. svr - Support Vector Regression stabreg - Stability Regression knn - k-nearest neighbours multi_reg - meta method for independent multiple output regression lms - least mean squared regression alr - "adaptive (via leave-one-out) kernel" ridge regression
Regression objects. gridsel - select parameters from a grid of values
Model Selection objects. one_class_svm- One class SVM kmeans - K means clustering hierarchical - Hierarchical clustering kpca - Kernel Principal Components Analysis spectral - Spectral clustering
Unsupervised objects. trans_prob - Transduction using iterative probability estimation trans_marg - Transduction by adding examples far from the margin See also help on: demos, train, test.
Semi-Supervised objects.
Adding new objects. See the tutorial section for guidelines on how to write a new object. Add it by emailing Jason Weston. Also send bug reports there.