A library for debugging/inspecting machine learning classifiers and explaining their predictions

👶 Technical concepts explained in layman terms! git.io/eli5

A library for debugging/inspecting machine learning classifiers and explaining their predictions

Scripts and links to recreate the ELI5 dataset.

The continuing increase of Internet of Things (IoT) based networks have increased the need for Computer networks intrusion detection systems (IDSs). Over the last few years, IDSs for IoT networks have been increasing reliant on machine learning (ML) techniques, algorithms, and models as traditional cybersecurity approaches become less viable for IoT. IDSs that have developed and implemented using machine learning approaches are effective, and accurate in detecting networks attacks with high-performance capabilities. However, the acceptability and trust of these systems may have been hindered due to many of the ML implementations being ‘black boxes’ where human interpretability, transparency, explainability, and logic in prediction outputs is significantly unavailable. The UNSW-NB15 is an IoT-based network traffic data set with classifying normal activities and malicious attack behaviors. Using this dataset, three ML classifiers: Decision Trees, Multi-Layer Perceptrons, and XGBoost, were trained. The ML classifiers and corresponding algorithm for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets proved to be very high-performing based on model performance accuracies. Thereafter, established Explainable AI (XAI) techniques using Scikit-Learn, LIME, ELI5, and SHAP libraries allowed for visualizations of the decision-making frameworks for the three classifiers to increase explainability in classification prediction. The results determined XAI is both feasible and viable as cybersecurity experts and professionals have much to gain with the implementation of traditional ML systems paired with Explainable AI (XAI) techniques.

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A set of tools for leveraging pre-trained embeddings, active learning and model explainability for effecient document classification

Interpreting Machine learning Models(ML Models,DL models) with Lime,Eli5,Shap,etc

Energy, Frequency, Vibration. It's my belief that we can use Bitcoin as a telephone equivalent to communicate through space/time (ELI5 - Communicate with future civilizations)

Code for blogpost https://zablo.net/blog/post/understanding-implementing-simclr-guide-eli5-pytorch/

Build a Web App called Menara to Predict, Forecast House Prices and search GreatSchools in California - Bay Area

Analyzing the Features which leads to heart diseases and visualizing the models' performance and important features using eli5, shap and pdp.

GRPO explained like ELI5. A technique used in DeepSeek models.

Detecting toxic comments with Keras and interpreting the model with ELI5

an implementation of the ansi c standard library that makes sense when ur stoned, tired, or otherwise inebriated

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ELIH: Explain Machine Learning predictions like I'm a human

2024 iThome ironman - ELI5 for GenAI and Software Development

Credit default detection with Ray, XGBoost, and ELI5

a demo of interpretability tool including eli5,pdp,shap,lime

ELI5 graphs (Explain Like I'm Five) for different Blockchain and DLTs projects

Self-explanatory. Demo at https://vzakharov.github.io/eli5

科技系懶人包

A community-driven project refining AI-generated explanations, addressing hallucinations, with user contributions refining content and adding new concepts.

PDF-to-Obsidian pipeline using MLX LLaMA with custom LoRA adapters for multi-perspective paper summaries (ELI5, Executive, Intuitive)

A Java command-line program that determines if a post on /r/eli5 is readable by a 5-year-old

A simple chrome extension for paraphrasing paragraphs on the interwebs :)

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scripts for reproducing ELI5-Category dataset, training and deploying LFQA models

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