A statistical and machine learning method for predicting a continuous numerical value based on the relationship between variables.
Regression models estimate how one or more input variables relate to an output. Linear regression, the simplest form, fits a straight line: for example, predicting a home's price based on its size, location, and age. More advanced forms include polynomial regression, decision tree regression, and neural network regression.
Regression is a core tool in predictive analytics for forecasting sales, estimating delivery times, or modeling demand. Logistic regression, despite its name, is used for classification, predicting probabilities of outcomes like yes or no. In software development, regression has a different meaning: a regression bug is when something that used to work breaks after a change, which is why teams run regression tests.
When a machine learning model learns its training data too closely, including noise, and performs poorly on new, unseen data.
Predictive AnalyticsUsing historical data, statistics, and machine learning to forecast future outcomes or behaviors.
Supervised LearningA machine learning approach where a model learns from labeled examples, each pairing an input with the correct output.
What load testing is, how load, stress, spike and soak tests differ, and how to read results like percentiles and error rates before real traffic finds your limits.
How to build a small test set and scoring rubric before you ship an AI feature, which grading methods to use, and how to keep quality from slipping when you change prompts or models.
A section-by-section guide to writing a tech resume that applicant tracking systems can read and hiring managers want to read, with before-and-after bullet examples.