From Words to Vectors: What Happens in Between?

Text is converted into numerical representations for machine learning models using a classic approach called TF-IDF vectorization. This method combines two metrics: Term Frequency (TF), which measures how often a word appears in a single document, and Inverse Document Frequency (IDF), which measures how rare or common that word is across all documents. By multiplying these two scores, a weight is assigned to each word that reflects its importance and distinctiveness within the dataset. The article provides a step-by-step guide with mathematical formulas and a simple dataset to demonstrate how to calculate these values and create vectors for text classification.