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Search scikit-learn , found 8 results

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Early Stopping Callback

early-stopping-callback

jeremylongshore/claude-code-plugins-plus-skills

Automated guidance for configuring and applying early stopping callbacks across ML training workflows, covering best practices, code generation, and validation for PyTorch, TensorFlow, and scikit-learn experiments.

Learning Rate Scheduler

learning-rate-scheduler

jeremylongshore/claude-code-plugins-plus-skills

Provides automated guidance for configuring learning rate schedulers during ML training, covering best practices, setup patterns, and framework-ready code snippets for PyTorch, TensorFlow, and Scikit-learn while validating against common standards.

MLflow Lifecycle Tracker

Orchestra-Research/AI-Research-SKILLs

MLflow provides a framework-agnostic ML lifecycle toolkit for tracking experiments, logging models/artifacts, managing registries with versions/stages, and deploying across platforms so teams can reproduce and collaborate on PyTorch, TensorFlow, and Scikit-Learn workflows.

Aeon Time Series Toolkit

K-Dense-AI/claude-scientific-skills

Aeon is a scikit-learn compatible toolkit for classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, and feature engineering on temporal data.

Molecular Featurization Hub

K-Dense-AI/claude-scientific-skills

Molfeat is a Python hub for turning SMILES or RDKit molecules into 100+ featurizers and pretrained embeddings, powering QSAR, screening, similarity, and deep learning workflows with transformers, calculators, caching, and parallel processing.

Scikit Learn ML Guide

K-Dense-AI/claude-scientific-skills

Comprehensive guidance for building classical machine learning workflows with scikit-learn, spanning supervised and unsupervised algorithms, preprocessing, evaluation, tuning, and production-ready pipelines for structured or text data.

Stable Baselines3 RL Toolkit

stable-baselines3

K-Dense-AI/claude-scientific-skills

Stable Baselines3 supplies PyTorch-based RL algorithms with a scikit-learn-style API for training agents, crafting Gymnasium environments, running vectorized workloads, wiring callbacks, and persisting models—ideal for single-agent experimentation and rapid RL prototyping.

UMAP Manifold Reduction

K-Dense-AI/claude-scientific-skills

UMAP-Learn delivers fast nonlinear dimensionality reduction for visualization, clustering prep, and feature engineering; it mirrors scikit-learn APIs and supports supervised/semi-supervised embeddings with tunable parameters.

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