Model Explainability

Python Programming Learning Circle

Feb 23, 2022 · Artificial Intelligence

A Survey of Python Libraries for Hyperparameter Optimization, Feature Selection, Model Explainability, and Rapid Machine Learning Development

This article introduces several Python libraries—including Optuna, ITMO_FS, shap‑hypertune, PyCaret, floWeaver, Gradio, Terality, and torch‑handle—that simplify hyperparameter tuning, feature selection, model explainability, visualization, and low‑code ML workflows, providing code examples and key advantages for each tool.

Hyperparameter OptimizationModel ExplainabilityPython

0 likes · 10 min read

A Survey of Python Libraries for Hyperparameter Optimization, Feature Selection, Model Explainability, and Rapid Machine Learning Development

Tencent Cloud Developer

Sep 23, 2020 · Artificial Intelligence

NLP Model Interpretability: White-box and Black-box Methods and Business Applications

The article reviews NLP interpretability techniques, contrasting white‑box approaches that probe model internals such as neuron analysis, diagnostic classifiers, and attention with black‑box strategies like rationales, adversarial testing, and local surrogates, and argues that black‑box methods are generally more practical for business deployment despite offering shallower insights.

Adversarial TestingBERTInterpretability

0 likes · 12 min read

NLP Model Interpretability: White-box and Black-box Methods and Business Applications