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Tag: tuning

  • Stop Tuning Hyperparameters. Start Tuning Your Problem.

    Stop Tuning Hyperparameters. Start Tuning Your Problem. 80% of ML projects fail from bad problem framing, not bad models. A 5-step protocol to define the right problem before you write training code. The post Stop Tuning Hyperparameters. Start Tuning Your Problem. appeared first on Towards Data Science. Kaushik Rajan Go to original source

  • A Theoretical Framework for LLM Fine-tuning Using Early Stopping for Non-random Initialization

    A Theoretical Framework for LLM Fine-tuning Using Early Stopping for Non-random Initialization arXiv:2602.13942v1 Announce Type: new Abstract: In the era of large language models (LLMs), fine-tuning pretrained models has become ubiquitous. Yet the theoretical underpinning remains an open question. A central question is why only a few epochs of fine-tuning are typically sufficient to achieve…

  • Fine Tuning a Simulation-Driven Estimator

    Fine Tuning a Simulation-Driven Estimator arXiv:2504.04480v2 Announce Type: cross Abstract: Many industries now deploy high-fidelity simulators (digital twins) to represent physical systems, yet their parameters must be calibrated to match the true system. This motivated the construction of simulation-driven parameter estimators, built by generating synthetic observations for sampled parameter values and learning a supervised mapping…

  • A Visual Guide to Tuning Random Forest Hyperparameters

    A Visual Guide to Tuning Random Forest Hyperparameters How hyperparameter tuning visually changes random forests The post A Visual Guide to Tuning Random Forest Hyperparameters appeared first on Towards Data Science. James Gibbins Go to original source

  • Marginal Effect of Hyperparameter Tuning with XGBoost

    Marginal Effect of Hyperparameter Tuning with XGBoost Demystifying Bayesian hyperparameter optimization and comparing hyperparameter tuning paradigms The post Marginal Effect of Hyperparameter Tuning with XGBoost appeared first on Towards Data Science. Noah Swan Go to original source

  • A Visual Guide to Tuning Decision-Tree Hyperparameters

    A Visual Guide to Tuning Decision-Tree Hyperparameters How hyperparameter tuning visually changes decision trees The post A Visual Guide to Tuning Decision-Tree Hyperparameters appeared first on Towards Data Science. James Gibbins Go to original source

  • Smarter Model Tuning: An AI Agent with LangGraph + Streamlit That Boosts ML Performance

    Smarter Model Tuning: An AI Agent with LangGraph + Streamlit That Boosts ML Performance Automating model tuning in Python with Gemini, LangGraph, and Streamlit for regression and classification improvements The post Smarter Model Tuning: An AI Agent with LangGraph + Streamlit That Boosts ML Performance appeared first on Towards Data Science. Gustavo Santos Go to…

  • Learning to Choose or Choosing to Learn: Best-of-N vs. Supervised Fine-Tuning for Bit String Generation

    Learning to Choose or Choosing to Learn: Best-of-N vs. Supervised Fine-Tuning for Bit String Generation arXiv:2505.17288v1 Announce Type: new Abstract: Using the bit string generation problem as a case study, we theoretically compare two standard methods for adapting large language models to new tasks. The first, referred to as supervised fine-tuning, involves training a new…

  • Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization

    Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization arXiv:2503.15704v1 Announce Type: new Abstract: The performance of sequential Monte Carlo (SMC) samplers heavily depends on the tuning of the Markov kernels used in the path proposal. For SMC samplers with unadjusted Markov kernels, standard tuning objectives, such as the Metropolis-Hastings acceptance rate or the…

  • Fine-tuning Multimodal Embedding Models

    Fine-tuning Multimodal Embedding Models Adapting CLIP to YouTube Data (with Python Code) This is the 4th article in a larger series on multimodal AI. In the previous post, we discussed multimodal RAG systems, which can retrieve and synthesize information from different data modalities (e.g. text, images, audio). There, we saw how we could implement such a…

  • The Next Frontier in LLM Accuracy

    The Next Frontier in LLM Accuracy Exploring the Power of Lamini Memory Tuning Image generated by DALL-E 3 Accuracy is often critical for LLM applications, especially in cases such as API calling or summarisation of financial reports. Fortunately, there are ways to enhance precision. The best practices to improve accuracy include the following steps: You can start…