tpot - A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming

•    Python

---

Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.

machine-learning data-science automl automation scikit-learn hyperparameter-optimization model-selection parameter-tuning automated-machine-learning random-forest gradient-boosting feature-engineering xgboost genetic-programming

yellowbrick - Visual analysis and diagnostic tools to facilitate machine learning model selection.

•    Python

---

Visual analysis and diagnostic tools to facilitate machine learning model selection. Image by Quatro Cinco, used with permission, Flickr Creative Commons.

machine-learning visual-analysis model-selection visualization scikit-learn visualizer matplotlib estimator residuals transformer advantage anaconda

pathpy - pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models

•    Python

---

pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models. The package is specifically tailored to analyze sequential data which capture multiple observations of short, independent paths observed in an underlying graph topology. Examples for such data include user click streams in information networks, biological pathways, or traces of information propagating in social media. Unifying the analysis of pathways and temporal networks, pathpy also supports the extraction of time-respecting paths from time-stamped network data. It extends (and will eventually supersede) the package pyTempnets.

temporal-networks pathways analysis sequential-data data data-mining network-analysis networks graph model-selection machine-learning graphical-models multi-order temporal-correlations

IQ-TREE - Efficient phylogenomic software by maximum likelihood

•    C++

---

The IQ-TREE software was created as the successor of IQPNNI and TREE-PUZZLE (thus the name IQ-TREE). IQ-TREE was motivated by the rapid accumulation of phylogenomic data, leading to a need for efficient phylogenomic software that can handle a large amount of data and provide more complex models of sequence evolution. To this end, IQ-TREE can utilize multicore computers and distributed parallel computing to speed up the analysis. IQ-TREE automatically performs checkpointing to resume an interrupted analysis. As input IQ-TREE accepts all common sequence alignment formats including PHYLIP, FASTA, Nexus, Clustal and MSF. As output IQ-TREE will write a self-readable report file (name suffix .iqtree), a NEWICK tree file (.treefile) which can be visualized by tree viewer programs such as FigTree, Dendroscope or iTOL.

maximum-likelihood phylogenomics phylogenetics mixture-model iq-tree ultrafast-bootstrap model-selection

SVR.jl - Support Vector Regression using libSVM

•    Julia

---

Support Vector Regression (SVR) analysis in Julia utilizing the libSVM library. SVR is a module of MADS (Model Analysis & Decision Support).

mads julia support-vector-machine regression model-assessment decision-support machine-learning high-performance-computing model-analysis model-reduction support-vector-regression support-vector-machines uncertainty-quantification sensitivity-analysis model-selection model-simulation model-predictions data-modeling data-analytics data-analysis