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Please cite our JMLR paper [bibtex]. Some parts of the package were created as part of other publications. If you use these parts, please cite the relevant work appropriately. An overview of all mlr related publications can be found here.

machine-learning data-science tuning cran r-package predictive-modeling classification regression statistics r survival-analysis imbalance-correction tutorial mlr learners hyperparameters-optimization feature-selection multilabel-classification clustering stackingThis repository contains Python code for a selection of tables, figures and LAB sections from the book 'An Introduction to Statistical Learning with Applications in R' by James, Witten, Hastie, Tibshirani (2013). 2018-01-15: Minor updates to the repository due to changes/deprecations in several packages. The notebooks have been tested with these package versions. Thanks @lincolnfrias and @telescopeuser.

machine-learning predictive-modeling islr statistical-learningSkater is a unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system often needed for real world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). The project was started as a research idea to find ways to enable better interpretability(preferably human interpretability) to predictive "black boxes" both for researchers and practioners. The project is still in beta phase.

ml predictive-modeling machine-learning modeling-tools model-interpretation blackbox datascience model-explanation explanation-system deep-learning deep-neural-networks attribution lstm-neural-networks cnn-classificationThis repository contains python implementations of certain exercises from the course by Andrew Ng. For a number of assignments in the course you are instructed to create complete, stand-alone Octave/MATLAB implementations of certain algorithms (Linear and Logistic Regression for example). The rest of the assignments depend on additional code provided by the course authors. For most of the code in this repository I have instead used existing Python implementations like Scikit-learn.

coursera-machine-learning predictive-modeling andrew-ngThis Node.js / io.js module provides access to the H2O JVM (and extensions thereof), its objects, its machine-learning algorithms, and modeling support (basic munging and feature generation) capabilities. It is designed to bring H2O to a wider audience of data and machine learning devotees that work exclusively with Javascript, for building machine learning applications or doing data munging in a fast, scalable environment without any extra mental anguish about threads and parallelism.

machine-learning predictive-analytics predictive-modeling data-mining computational-statistics statistics statistical-learning clustering classification regression deep-learningThis book is now available at Amazon in [Kindle]( Link: http://a.co/d/dIj1XwD) Black & White and color 📗 🚀. Most of the written R code can be used in real scenarios! I worked on the funModeling R package at the same time, so it is used many times in the book.

machine-learning data-preparation data-science big-data data-analysis learning predictive-modeling predictive-analysis visualization statistics descriptive-statistics analyticsImplementation of basic pattern recognition for anomaly detection. Implementation of analytics unit for Hastic.

timeseries analytics anomaly-detection monitor monitoring-server hastic-server alerting pattern-recognition selfhosted self-hosted docker grafana pattern-detection predictive-modeling monitoring-tool monitoring
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