Collection of classifiers to ease the exploration.

Inheritance diagram of mvpa2.clfs.warehouse


absolute_features() Returns a mapper that converts features into absolute values.
is_sequence_type isSequenceType(a) – Return True if a has a sequence type, False otherwise.
maxofabs_sample() Returns a mapper that finds max of absolute values of all samples.


BLR([sigma_p, sigma_noise]) Bayesian Linear Regression (BLR).
FeatureSelectionClassifier(clf, mapper, \*\*kwargs) This is nothing but a MappedClassifier.
FixedNElementTailSelector(nelements, \*\*kwargs) Given a sequence, provide set of IDs for a fixed number of to be selected elements.
FractionTailSelector(felements, \*\*kwargs) Given a sequence, provide Ids for a fraction of elements
GNB(\*\*kwargs) Gaussian Naive Bayes Classifier.
GPR([kernel]) Gaussian Process Regression (GPR).
GeneralizedLinearKernel(\*args, \*\*kwargs) The linear kernel class.
LDA(\*\*kwargs) Linear Discriminant Analysis.
LinearCSVMC([C]) C-SVM classifier using linear kernel.
LinearKernel(\*args, \*\*kwargs) Simple linear kernel: K(a,b) = a*b.T
LinearLSKernel(\*args, \*\*kwargs) A simple Linear kernel: K(a,b) = a*b.T
LinearNuSVMC([nu]) Nu-SVM classifier using linear kernel.
LinearSVMKernel alias of LinearLSKernel
MulticlassClassifier(clf[, bclf_type]) Perform multiclass classification using a list of binary classifiers.
OddEvenPartitioner([usevalues]) Create odd and even partitions based on a sample attribute.
OneWayAnova([space]) FeaturewiseMeasure that performs a univariate ANOVA.
PLR([lm, criterion, reduced, maxiter]) Penalized logistic regression Classifier.
PolyLSKernel(\*\*kwargs) Polynomial kernel: K(a,b) = (gamma*a*b.T + coef0)**degree
QDA(\*\*kwargs) Quadratic Discriminant Analysis.
RandomClassifier(\*\*kwargs) Dummy classifier deciding on labels absolutely randomly
RangeElementSelector([lower, upper, ...]) Select elements based on specified range of values
RbfCSVMC([C]) C-SVM classifier using a radial basis function kernel
RbfLSKernel(\*\*kwargs) Radial Basis Function kernel (aka Gaussian):
RbfNuSVMC([nu]) Nu-SVM classifier using a radial basis function kernel
RbfSVMKernel alias of RbfLSKernel
RegressionAsClassifier(clf[, centroids, ...]) Allows to use arbitrary regression for classification.
SMLR(\*\*kwargs) Sparse Multinomial Logistic Regression Classifier.
SMLRWeights(clf[, force_train]) SensitivityAnalyzer that reports the weights SMLR trained
SVM(\*\*kwargs) Support Vector Machine Classifier.
SensitivityBasedFeatureSelection(...[, ...]) Feature elimination.
SigmoidLSKernel(\*\*kwargs) Sigmoid kernel: K(a,b) = tanh(gamma*a*b.T + coef0)
SplitClassifier(clf[, partitioner, splitter]) BoostedClassifier to work on splits of the data
SplitRFE(lrn, partitioner, fselector[, ...]) RFE with the nested cross-validation to estimate optimal number of features.
SquaredExponentialKernel([length_scale, sigma_f]) The Squared Exponential kernel class.
Warehouse([known_tags, matches]) Class to keep known instantiated classifiers
kNN([k, dfx, voting]) k-Nearest-Neighbour classifier.