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
GLMNET_C(\*\*kwargs) GLM-NET Multinomial Classifier.
GLMNET_R(\*\*kwargs) GLM-NET Gaussian Regression Classifier.
GNB(\*\*kwargs) Gaussian Naive Bayes Classifier.
GPR([kernel]) Gaussian Process Regression (GPR).
GeneralizedLinearKernel(\*args, \*\*kwargs) The linear kernel class.
LARS([model_type, trace, normalize, ...]) Least angle regression (LARS).
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.
LinearSGKernel([normalizer_cls, normalizer_args]) A basic linear kernel computed via Shogun: K(a,b) = a*b.T
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
PolySGKernel(\*\*kwargs) Polynomial kernel: K(a,b) = (a*b.T + c)**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
RbfSGKernel(\*\*kwargs) Radial basis function: K(a,b) = exp(-||a-b||**2/sigma)
RbfSVMKernel alias of RbfLSKernel
RegressionAsClassifier(clf[, centroids, ...]) Allows to use arbitrary regression for classification.
SKLLearnerAdapter(skl_learner[, tags, ...]) Generic adapter for instances of learners provided by scikits.learn
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.
sklElasticNet alias of ElasticNet
sklExtraTreesClassifier alias of ExtraTreesClassifier
sklExtraTreesRegression alias of ExtraTreesRegressor
sklLDA alias of LinearDiscriminantAnalysis
sklLars alias of Lars
sklLassoLars alias of LassoLars
sklLassoLarsIC alias of LassoLarsIC
sklPLSRegression alias of PLSRegression
sklRandomForestClassifier alias of RandomForestClassifier
sklRandomForestRegression alias of RandomForestRegressor