high-performance Python package for predictive modeling

 mlpy provides high level procedures that support, with few lines of
 code, the design of rich Data Analysis Protocols (DAPs) for
 preprocessing, clustering, predictive classification and feature
 selection. Methods are available for feature weighting and ranking,
 data resampling, error evaluation and experiment landscaping.
 .
 mlpy includes: SVM (Support Vector Machine), KNN (K Nearest
 Neighbor), FDA, SRDA, PDA, DLDA (Fisher, Spectral Regression,
 Penalized, Diagonal Linear Discriminant Analysis) for classification
 and feature weighting, I-RELIEF, DWT and FSSun for feature weighting,
 *RFE (Recursive Feature Elimination) and RFS (Recursive Forward
 Selection) for feature ranking, DWT, UWT, CWT (Discrete, Undecimated,
 Continuous Wavelet Transform), KNN imputing, DTW (Dynamic Time
 Warping), Hierarchical Clustering, k-medoids, Resampling Methods,
 Metric Functions, Canberra indicators.