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Badge](https://api.codacy.com/project/badge/Grade/810ed0d38e6f47079eab3426f6bf6f95)](https://www.codacy.com/app/m-capuccini/scala-cp?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=mcapuccini/scala-cp\u0026amp;utm_campaign=Badge_Grade)\n\nScala-CP is a Scala implementation of the Conformal Prediction (CP) framework, introduced by Vovk *et. al.* in the book Algorithmic Learning in a Random World. When assigning confidence to machine learning models, CP is a nice alternative to cross-validation. Instead of predicting a value for a certain feature vector, a conformal predictor outputs a prediction set/region that contains the correct prediction with probability *1-𝜺*, where *𝜺* is a user-defined significance level. The choose of the significance level will of course influence the size of the prediction set/region. In alternative, using CP one can predict object-specific p-values for unseen examples.\n\n## Table of Contents\n- [Getting started](#getting-started)\n- [Documentation](#documentation)\n- [Examples](#examples)\n  - [Scala-CP with Spark MLlib](https://github.com/mcapuccini/scala-cp/blob/master/cp/src/test/scala/se/uu/it/cp/SparkTest.scala)\n  - [Scala-CP with LIBLINEAR](https://github.com/mcapuccini/scala-cp/blob/master/cp/src/test/scala/se/uu/it/cp/LibLinTest.scala)\n  - [ZeppelinHub: Scala-CP with Spark MLlib](https://www.zepl.com/viewer/notebooks/bm90ZTovL21hcmNvY2FwL3plcHBlbGluLWNwLzUyNjVlOGQyYjkxOTRmNGU4MWM4OGJjMzQyMDMzZDk5L25vdGUuanNvbg)\n- [List of publications](#list-of-publications)\n- [Roadmap](#roadmap)\n\n## Getting started\nScala-CP can be used along with any Scala/Java machine learning library and algorithm. All you have to do is to add the Scala-CP dependency to your *pom.xml* file:\n\n```xml\n\u003cdependencies\u003e\n  ...\n  \u003cdependency\u003e\n    \u003cgroupId\u003ese.uu.it\u003c/groupId\u003e\n    \u003cartifactId\u003ecp\u003c/artifactId\u003e\n    \u003cversion\u003e0.1.0\u003c/version\u003e\n  \u003c/dependency\u003e\n  ...\n\u003c/dependencies\u003e\n```\n\n## Documentation\nThe API documentation is available at: https://mcapuccini.github.io/scala-cp/scaladocs/. \n\n## Examples\nFor some usage examples please refer to the unit tests:\n\n  - [Scala-CP with Spark MLlib](https://github.com/mcapuccini/scala-cp/blob/master/cp/src/test/scala/se/uu/it/cp/SparkTest.scala)\n  - [Scala-CP with LIBLINEAR](https://github.com/mcapuccini/scala-cp/blob/master/cp/src/test/scala/se/uu/it/cp/LibLinTest.scala)\n  \nYou can also refer to this Apache Zeppelin notebooks for more examples:\n\n  - [ZeppelinHub: Scala-CP with Spark MLlib](https://www.zepl.com/viewer/notebooks/bm90ZTovL21hcmNvY2FwL3plcHBlbGluLWNwLzUyNjVlOGQyYjkxOTRmNGU4MWM4OGJjMzQyMDMzZDk5L25vdGUuanNvbg)\n  \n## List of publications\n- [M. Capuccini, L. Carlsson, U. Norinder and O. Spjuth, \"Conformal Prediction in Spark: Large-Scale Machine Learning with Confidence,\" 2015 IEEE/ACM 2nd International Symposium on Big Data Computing (BDC), Limassol, 2015, pp. 61-67.](http://ieeexplore.ieee.org/document/7406330/)\n- [Ahmed, L., Georgiev, V., Capuccini, M., Toor, S., Schaal, W., Laure, E., \u0026 Spjuth, O. (2018). Efficient iterative virtual screening with Apache Spark and conformal prediction. Journal of cheminformatics, 10(1), 1-8.](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0265-z)\n\n## Roadmap\n\n### Inductive Conformal Prediction\n- [x] Classification \n- [ ] Regression\n\n### Transductive Conformal Prediction\n- [ ] Classification \n- [ ] Regression\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmcapuccini%2Fscala-cp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmcapuccini%2Fscala-cp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmcapuccini%2Fscala-cp/lists"}