Similarity Measures and Loss Functions in Machine Learning
Dive into Bhattacharyya distance, loss functions such as MSE and cross-entropy, and their applications in optimizing machine learning models for classification and regression.
Recent posts
Understanding Markov Systems
Introduction
Regularization in Machine Learning
Introduction
Detect Multivariate Data Drift
In machine learning, ensuring the ongoing accuracy and reliability of models in production is paramount. One significant challenge faced by data scientists and engineers is data drift, where the statistical properties of the input data change over time, leading to potential degradation in model p...
Automating Feature Engineering
Feature engineering is a critical step in the machine learning pipeline, involving the creation, transformation, and selection of variables (features) that can enhance the predictive performance of models. This process requires deep domain knowledge and creativity to extract meaningful informatio...
Navigating AI Fairness
Introduction
From Data to Probability
In statistics, the P Value is a fundamental concept that plays a crucial role in hypothesis testing. It quantifies the probability of observing a test statistic at least as extreme as the one observed, assuming the null hypothesis is true. Essentially, the P Value helps us assess whether the obse...
Kullback-Leibler and Wasserstein Distances
In mathematics, the concept of “distance” extends beyond the everyday understanding of the term. Typically, when we think of distance, we envision Euclidean distance, which is the straight-line distance between two points in space. This form of distance is familiar and intuitive, often represente...
Survival Analysis in Management
Explore the role of survival analysis in management, focusing on time-to-event data and techniques like the Kaplan-Meier estimator and Cox proportional hazards model for business decision-making.