Introducing ikNN: An Interpretable k Nearest Neighbors Model
Introducing ikNN: An Interpretable k Nearest Neighbors Model
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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 it...
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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...
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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 le...
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Interpretable Outlier Detection with Counts Outlier Detector (COD)
Overview of the Counts Outliers Detector (COD)
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Applying Einstein's Principle of Simplicity Across Disciplines
Albert Einstein's quote, "Everything should be made as simple as possible, but not simpler," encapsulates a fundamental principle in science and analytics. I...
Read articleTesting and Evaluating Outlier Detectors Using Doping
Outlier detection presents significant challenges, particularly in evaluating the effectiveness of outlier detection algorithms. Traditional methods of evalu...
Read articleCopula, GARCH, and Other Financial Models
An in-depth look at financial models such as Copula and GARCH, their importance in quantitative analysis, and practical applications with Python.
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Understanding Uncertainty in Statistical Estimates: Confidence and Prediction Intervals
Statistical estimates always have some uncertainty. Consider a simple example of modeling house prices based solely on their area using linear regression. A ...
Read articleDisaggregating Energy Consumption: The NILM Algorithms
Non-intrusive load monitoring (NILM) is an advanced technique that disaggregates a building's total energy consumption into the usage patterns of individual ...
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