Required for a machine learning pipeline data preprocessing and variable engineering scriptneeds to be prepared. When the dataset is passed through this script, the modeling starts.expected to be ready. Dataset Story The data set is the data set of the people who were in the Titanic shipwreck.It consists of 768 observations and 12 variables.The target variable is specified as “Survived”;1: one’s survival,0: indicates the person’s inability to survive. Variables PassengerId: ID of the passenger Survived: Survival status (0: not survived, […]

A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery. Sun, F., Liu, Y. and Sun, H., 2021. Physics-informed Spline Learning for Nonlinear Dynamics Discovery, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) https://www.ijcai.org/proceedings/2021/0283.pdf 3 nonlinear dynamics examples: Lorenz system (single and multi data) Double pendulum system Electro-Mechanical Positioning system GitHub https://github.com/andyfangzheng/PiSL    

Pneumonia Detection using machine learning. Training was done in colab: DEMO: Result (Confusion Matrix): Data I uploaded my dataset to kaggle I used a modified version of this dataset from kaggle. Instead of NORMAL and PNEUMONIA I split the PNEUMONIA dataset to BACTERIAL PNUEMONIA and VIRAL PNEUMONIA. This way the data is more evenly distributed and I can distinguish between viral and bacterial pneumonia. I also combined the validation dataset with the test dataset because the validation dataset only had […]

themis-ml is a Python library built on top of pandas and sklearnthat implements fairness-aware machine learning algorithms. themis-ml defines discrimination as the preference (bias) for or against a set of social groups that result in the unfair treatment of its members with respect to some outcome. It defines fairness as the inverse of discrimination, and in the context of a machine learning algorithm, this is measured by the degree to which the algorithm’s predictions favor one social group over another […]

This project is about explaining what machine learning classifiers (or models) are doing. At the moment, we support explaining individual predictions for text classifiers or classifiers that act on tables (numpy arrays of numerical or categorical data) or images, with a package called lime (short for local interpretable model-agnostic explanations). Lime is based on the work presented in this paper (bibtex here for citation). Here is a link to the promo video: Our plan is to add more packages that […]

ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions. It provides support for the following machine learning frameworks and packages: scikit-learn. Currently ELI5 allows to explain weights and predictions of scikit-learn linear classifiers and regressors, print decision trees as text or as SVG, show feature importances and explain predictions of decision trees and tree-based ensembles. ELI5 understands text processing utilities from scikit-learn and can highlight text data accordingly. Pipeline and FeatureUnion are supported. […]

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations). Install SHAP can be installed from either PyPI or conda-forge: pip install shap or conda install -c conda-forge shap Tree ensemble example (XGBoost/LightGBM/CatBoost/scikit-learn/pyspark models) While SHAP can explain the output of any machine learning model, we […]

LibFewShot Make few-shot learning easy. Supported Methods Quick Installnation Please refer to install.md(安装) for installation. Full tutorials can be found at document(中文文档). Contributing Feel free to contribute any kind of function or enhancement, here the coding style follows PEP8. Please refer to contributing.md(贡献代码) for the contributing guideline. License This project is licensed under the MIT License. See LICENSE for more details Acknowledgement LibFewShot is an open source project designed to help few shot learning researchers quickly understand the classic methods […]

Embeddinghub is a database built for machine learning embeddings. It is built with four goals in mind. Store embeddings durably and with high availability Allow for approximate nearest neighbor operations Enable other operations like partitioning, sub-indices, and averaging Manage versioning, access control, and rollbacks painlessly Features Supported Operations: Run approximate nearest neighbor lookups, average multiple embeddings, partition tables (spaces), cache locally while training, and more. Storage: Store and index billions vectors embeddings from our storage layer. Versioning: Create, manage, and […]

GitHub – kittupriyatham/Machine-Learning-Model-Deployment: This is a machine learning model deployment project of Iris classification model in a minimal UI using flask web framework and deployed it in Azure cloud using Azure app service. We initially made this project as a requirement for an internship at Indian Servers. We are now making it open to contribution. This is a machine learning model deployment project of Iris classification model in a minimal UI using flask web framework and deployed it in Azure […]