canberra
high-performance Python package for predictive modeling
Install
- All systems
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curl cmd.cat/canberra.sh
- Debian
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apt-get install python-mlpy - Ubuntu
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apt-get install python-mlpy - Windows (WSL2)
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sudo apt-get updatesudo apt-get install python-mlpy - Raspbian
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apt-get install python-mlpy - Dockerfile
- dockerfile.run/canberra
python-mlpy
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.