Collection of classifiers to ease the exploration.
Functions
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. |
Classes
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. |