Supervised Learning
A type of machine learning where the model is trained on labeled data — input-output pairs where the correct answer is provided. The model learns to map inputs to outputs and can then predict outputs for new, unseen inputs.
Why It Matters
Supervised learning is the most widely used ML approach in production systems, powering email spam filters, credit scoring, medical diagnosis, and image recognition.
Example
Training a model with thousands of emails labeled as 'spam' or 'not spam' so it can automatically classify new incoming emails.
Think of it like...
Like a student learning with an answer key — they practice problems where they can check if they got the right answer, gradually improving until they can solve new problems on their own.
Related Terms
Classification
A type of supervised learning task where the model predicts which category or class an input belongs to. The output is a discrete label rather than a continuous value.
Regression
A type of supervised learning task where the model predicts a continuous numerical value rather than a discrete category. The output can be any number within a range.
Training Data
The dataset used to teach a machine learning model. It contains examples (and often labels) that the model learns patterns from during the training process. The quality and quantity of training data directly impact model performance.
Overfitting
When a model learns the training data too well — including its noise and random fluctuations — and performs poorly on new, unseen data. The model essentially memorizes rather than generalizes.