Support Vector Machine (SVM) algorithm with a focus on classification tasks, using a simple 2D dataset for illustration. It explains key concepts like hard and soft margins, support vectors, kernel tricks, and optimization probles.
This article explores the use of Isolation Forest for anomaly detection and how SHAP (KernelSHAP and TreeSHAP) can be applied to explain the anomalies detected, providing insights into which features contribute to anomaly scores.
A beginner-friendly guide to AI development with Python, covering basics and sharing a concrete example with code.
This article provides a beginner-friendly introduction to HDBSCAN, a powerful hierarchical clustering algorithm that extends the capabilities of DBSCAN by handling varying densities more effectively. It compares HDBSCAN to DBSCAN and KMeans, highlighting the advantages of HDBSCAN in handling clusters of different shapes and sizes.
This article explains how adding monotonic constraints to traditional ML models can make them more reliable for causal inference, illustrated with a real estate example.
An overview of clustering algorithms, including centroid-based (K-Means, K-Means++), density-based (DBSCAN), hierarchical, and distribution-based clustering. The article explains how each type works, its pros and cons, provides code examples, and discusses use cases.
This article provides a step-by-step guide on how to extract meaningful features from graphs using NetworkX for machine learning applications. It uses Zachary's Karate Club Network as an example and covers feature extraction at node, edge, and graph levels.
This article details a data-driven exploration of owl species, using Wikipedia data to create a network visualization of owl relationships.
This article introduces interpretable clustering, a field that aims to provide insights into the characteristics of clusters formed by clustering algorithms. It discusses the limitations of traditional clustering methods and highlights the benefits of interpretable clustering in understanding data patterns.
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