Stratified Sampling
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Recent posts
Understanding t-SNE
In data analysis and machine learning, the challenge of making sense of large volumes of high-dimensional data is ever-present. Dimensionality reduction, a critical technique in data science, addresses this challenge by simplifying complex datasets into more manageable and interpretable forms wit...
Kernel Clustering in R
Clustering is one of the most fundamental techniques in data analysis and machine learning. It involves grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups. This is widely used across various fields...
The History of Artificial Intelligence
The History of Artificial Intelligence
Validating Anomaly Detection Models: Lessons from COPOD
Discover critical lessons learned from validating COPOD, a popular anomaly detection model, through test-driven validation techniques. Avoid common pitfalls in anomaly detection modeling.
Climate Value at Risk (VaR): A Data Science Perspective
Exploring Climate Value at Risk (VaR) from a data science perspective, detailing its role in assessing financial risks associated with climate change.
Advanced Sequential Change-Point Detection for Univariate Models
Sequential change-point detection plays a crucial role in real-time monitoring across industries. Learn about advanced methods, their practical applications, and how they help detect changes in univariate models.
Ethical Considerations in AI-Powered Elderly Care
As AI revolutionizes elderly care, ethical concerns around privacy, autonomy, and consent come into focus. This article explores how to balance technological advancements with the dignity and personal preferences of elderly individuals.
Paths of Combinatorics and Probability
Dive into the intersection of combinatorics and probability, exploring how these fields work together to solve problems in mathematics, data science, and beyond.