In statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.).
Classification is most likely to appear on Data Scientist job descriptions where we found it mentioned 12.1 percent of the time.
Cross-validation with XGBoost — Enhancing Customer Churn Classification with Tidymodels
Towards Data Science - Medium
Multilingual RAG, Algorithmic Thinking, Outlier Detection, and Other Problem-Solving Highlights
Towards Data Science - Medium
Building and Evaluating Classification Models to Predict Customer Churn with Tidymodels
Towards Data Science - Medium
Interpretable kNN (ikNN)
Towards Data Science - Medium
Machine Learning on GCP: From Notebooks to Pipelines
Towards Data Science - Medium