mailitics

Category: classification

  • 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…

  • Accuracy Is Dead: Calibration, Discrimination, and Other Metrics You Actually Need

    Accuracy Is Dead: Calibration, Discrimination, and Other Metrics You Actually Need A deep dive into advanced evaluation for data scientists The post Accuracy Is Dead: Calibration, Discrimination, and Other Metrics You Actually Need appeared first on Towards Data Science. Pol Marin Go to original source

  • Pairwise Cross-Variance Classification

    Pairwise Cross-Variance Classification Multi-class zero-shot embedding classification and error checking The post Pairwise Cross-Variance Classification appeared first on Towards Data Science. Doster Esh Go to original source

  • Retrieval Augmented Classification: Improving Text Classification with External Knowledge

    Retrieval Augmented Classification: Improving Text Classification with External Knowledge Text Classification stands as one of the most basic yet most important applications of natural language processing. It has a vital role in many real-world applications that go from filtering unwanted emails like spam, detecting product categories or classifying user intent in a chat-bot application. The…

  • Choosing Classification Model Evaluation Criteria

    Choosing Classification Model Evaluation Criteria Is Recall / Precision better than Sensitivity / Specificity? Continue reading on Towards Data Science » Viyaleta Apgar Go to original source

  • Machine Learning: From 0 to Something

    Machine Learning: From 0 to Something How I learned ML foundations to tackle a complex problem Continue reading on Towards Data Science » Ricardo Ribas Go to original source

  • Your Classifier Is Broken, But It Is Still Useful

    Your Classifier Is Broken, But It Is Still Useful When you run a binary classifier over a population you get an estimate of the proportion of true positives in that population. This is known as the prevalence. Photo by Rod Long on Unsplash But that estimate is biased, because no classifier is perfect. For example, if…

  • Machine Learning + openAI: solving a text classification problem

    Machine Learning + openAI: solving a text classification problem How I migrated an old solution to a more elegant, robust and scalable solution using text classification from openAI Continue reading on Towards Data Science » Ricardo Ribas Go to original source

  • Model Calibration, Explained: A Visual Guide with Code Examples for Beginners

    Model Calibration, Explained: A Visual Guide with Code Examples for Beginners MODEL EVALUATION & OPTIMIZATION When all models have similar accuracy, now what? You’ve trained several classification models, and they all seem to be performing well with high accuracy scores. Congratulations! But hold on — is one model truly better than the others? Accuracy alone doesn’t tell the…

  • Mastering Model Uncertainty: Thresholding Techniques in Deep Learning

    Mastering Model Uncertainty: Thresholding Techniques in Deep Learning Image generated by Dall-e A few words on thresholding, the softmax activation function, introducing an extra label, and considerations regarding output activation functions. In many real-world applications, machine learning models are not designed to make decisions in an all-or-nothing manner. Instead, there are situations where it is more…

  • Track Computer Vision Experiments with MLflow

    Track Computer Vision Experiments with MLflow Discover how to set up an efficient MLflow environment to track your experiments, compare and choose the best model for deployment Continue reading on Towards Data Science » Yağmur Çiğdem Aktaş Go to original source

  • A New Approach to AI Safety: Layer Enhanced Classification (LEC)

    A New Approach to AI Safety: Layer Enhanced Classification (LEC) LEC surpasses best in class models, like GPT-4o, by combining the efficiency of a ML classifier with the language understanding of an LLM Imagine sitting in a boardroom, discussing the most transformative technology of our time — artificial intelligence — and realizing we’re riding a rocket with no reliable safety…