Machine learning models are powerful tools for extracting patterns from data and making predictions. However, two critical challenges—overfitting and underfitting—can significantly impact a model’s performance. In this article, we’ll explore what overfitting and underfitting are, their causes, and practical techniques to address them. Whether you’re a beginner or experienced practitioner, understanding these concepts is essential ...
As datasets grow more complex with increasing features or dimensions, data scientists often face the curse of dimensionality—a phenomenon where high-dimensional data leads to issues like overfitting, increased computational cost, and reduced model accuracy. The more dimensions a dataset has, the harder it becomes to obtain statistically meaningful insights, and algorithms must process a much ...
What is Feature Engineering? In the world of machine learning, raw data alone isn’t enough to build successful models. This is where feature engineering comes in, often referred to as the “secret weapon” that transforms raw data into meaningful features, ultimately driving better model performance. Feature engineering is the process of selecting, modifying, and creating ...
Team Applied AI
Abhimanyu Saxena
