Binary Classification: Explained
Learn the core concepts of binary classification, explore common algorithms like Decision Trees and SVMs, and discover how to evaluate performance using precision, recall, and F1-score.
Recent posts
Understanding the Difference Between Regression and Path Analysis
Regression and path analysis are two statistical techniques used to model relationships between variables. This article explains their differences, highlighting key features and use cases for each.
Ethics in Data Science
A deep dive into the ethical challenges of data science, covering privacy, bias, social impact, and the need for responsible AI decision-making.
Applying R Functions on Rolling Windows Using the runner Package
Explore the runner package in R, which allows applying any R function to rolling windows of data with full control over window size, lags, and index types.
Multivariate Analysis of Variance (MANOVA) vs. ANOVA: When to Analyze Multiple Dependent Variables
Learn the key differences between MANOVA and ANOVA, and when to apply them in experimental designs with multiple dependent variables, such as clinical trials.
The Life and Legacy of Paul Erdős
Delve into the fascinating life of Paul Erdős, a wandering mathematician whose love for numbers and collaboration reshaped the world of mathematics.
The Vulnerability of Large Language Models to the Closure of Open-Source Data Platforms
An in-depth exploration of how the closure of open-source data platforms threatens the growth of Large Language Models and the vital role humans play in this ecosystem.
Demystifying Data Science
Discover how data science, a multidisciplinary field combining statistics, computer science, and domain expertise, can drive better business decisions and outcomes.
Exploring Shared Nearest Neighbors (SNN) for Outlier Detection
SNN is a distance metric that enhances traditional methods like k Nearest Neighbors, especially in high-dimensional, variable-density datasets.
Gaussian Processes for Time-Series Analysis in Python
Dive into Gaussian Processes for time-series analysis using Python, combining flexible modeling with Bayesian inference for trends, seasonality, and noise.