TLDR (Twin Learning for Dimensionality Reduction) is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self-supervised learning losses. Inspired by manifold learning, TLDR uses nearest neighbors as a way to build pairs from a training set and a redundancy reduction loss to learn an encoder that produces representations invariant across such pairs. Similar to other neighborhood embeddings, TLDR effectively and unsupervisedly learns low-dimensional spaces where local neighborhoods of the input space […]

Dual Adaptive Sampling for Machine Learning Interatomic potential. How to cite If you use this code in your research, please cite this using: Hongliang Yang, Yifan Zhu, Erting Dong, Yabei Wu, Jiong Yang, and Wenqing Zhang. Dual adaptive sampling and machine learning interatomic potentials for modeling materials with chemical bond hierarchy. Phys. Rev. B 104, 094310 (2021). Install Install pymtp You should first install the python interface for mtp: https://github.com/hlyang1992/pymtp Install das You can download the code by

In this project, the dataset was created through a survey opened on Google forms.The purpose of the form is to find the person’s favorite shopping type based on the information provided. In this context, 13 questions were asked to the user.As a result of these questions, the estimation of the shopping type, which is a classification problem, will be carried out with 5 different algorithms. These algorithms; Logistic Regression Random Forest Classifier Support Vector Machine K Neighbors Decision Tree algorithms […]

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 […]