Detecting Concept Drift in Machine Learning
A concise guide to concept drift detection, including drift types, DDM, evaluation datasets, and practical production monitoring steps.
Recent writing
Understanding Data Leakage in Machine Learning: Causes, Types, and Prevention
Imagine building a model to predict house prices based on features like size, location, and amenities. If you accidentally include the actual selling price during training, the model learns this private information instead of the underlying patterns in the other features. This is data leakage, co...
Building Custom Python Libraries for Your Industry Needs
A guide on developing custom Python libraries to meet specific industry needs, focusing on software development and automation.
Understanding Drift in Machine Learning: Detection, Diagnosis, and Response
Machine learning drift happens when the data, labels, or real-world relationship a model depends on changes after deployment.
Solow Growth Model and Extensions: Technological Change and Human Capital
An exploration of the Solow Growth Model’s extensions, including the effects of technological advancement and human capital on economic growth.
Introducing ikNN: An Interpretable k Nearest Neighbors Model
Introducing ikNN: An Interpretable k Nearest Neighbors Model
Frequent Patterns Outlier Factor
Outlier detection is a critical task in machine learning, particularly within unsupervised learning, where data labels are absent. The goal is to identify items in a dataset that deviate significantly from the norm. This technique is essential across numerous domains, including fraud detection, s...
Central Limit Theorem for m-dependent Random Variables Under Sub-linear Expectations
This article rigorously explores the Central Limit Theorem for m-dependent random variables under sub-linear expectations, presenting new inequalities, proof outlines, and implications in modeling dependent sequences.
Detecting Outliers Using Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is best known as a dimensionality reduction technique, but the same machinery detects outliers. The idea is direct: PCA learns the subspace the bulk of the data occupies, and points that do not fit that subspace stand out.
Interpretable Outlier Detection with Counts Outlier Detector (COD)
Overview of the Counts Outliers Detector (COD)