A technical analysis using R to classify iris images from a dataset, applying PCA and LDA for machine learning classification.
A tutorial on fine-tuning the ModernBERT model for classification tasks to build an efficient LLM router, covering setup, training, and evaluation.
A guide to transforming pretrained LLMs into text classifiers, with insights from the author's new book on building LLMs from scratch.
A guide to effective and ineffective evaluation methods for LLMs on tasks like classification, summarization, and translation, including practical metrics.
Introduces the Virtual Worlds Type Indicator (VWTI), a framework for categorizing different types of metaverses and virtual worlds.
Explores the challenge of machine learning models recognizing 'unknown' inputs, using mushroom classification as an example.
Overview of new features, changes, and fixes in PHP-ML 0.7.0, a machine learning library for PHP developers.
A guide to implementing logistic regression with gradient descent in JavaScript to solve classification problems.
Critique of the classic iris dataset as a misleading example in modern machine learning education, exploring its original scientific purpose.
A technical critique of the Net Reclassification Index (NRI), a statistical measure for evaluating prediction model improvements, highlighting its surprising biases.
A guide to implementing a weighted majority rule ensemble classifier in scikit-learn to combine different ML models and improve prediction accuracy.
A guide to building a weighted majority rule ensemble classifier in scikit-learn, demonstrated using the Iris dataset.
A developer shares a data mining project that builds a machine learning model to classify songs as happy or sad based on their lyrics.
An overview of predictive modeling, supervised machine learning, and pattern classification concepts, workflows, and applications.
A technical guide to Linear Discriminant Analysis (LDA) for dimensionality reduction and classification in machine learning, including a Python implementation.
A technical guide to Linear Discriminant Analysis (LDA) for dimensionality reduction and classification in machine learning, with comparisons to PCA.
An author critiques the overuse of PCA in data science, arguing it's not a universal solution for classification problems.
