# Classification regression and learning types

Subject: Deep Learning

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/#tee/read/4/tee-c4-s2

Canonical: https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c4-s2/

**Definition.** A learning problem is specified by its data, desired output and objective. Classification estimates a discrete label or class distribution; regression estimates numerical responses. The supervision type describes the feedback available during training.

### Explanation and working

**Regression** predicts a numerical quantity, such as temperature, price or a continuous rating. **Classification** predicts a category, such as defective/non-defective or cat/dog/bird. Binary classification has two classes. Multiclass classification chooses one of several classes. Multilabel classification can choose several labels together, such as “beach” and “sunset” for the same image.

![Look at the required answer: a numerical quantity is regression; a category is classification.](https://deep-learning-study-room.vercel.app/assets/figures/v_tasks.svg)

Diagram: Look at the required answer: a numerical quantity is regression; a category is classification.. Labels: What kind of answer do you need? | What kind of answer do you need? | The output decides whether this is regression or classification. | REGRESSION: a number | Input: house features | Output: predicted price | Example: Rs 50 lakh | CLASSIFICATION: a category | Input: an image | Output: cat / dog / bird | Example: dog | Supervised training supplies correct input–target pairs for these tasks.

Look at the required answer: a numerical quantity is regression; a category is classification. Tap the diagram to enlarge.

**Supervised learning** uses known input–target pairs. Most examination numericals in this course use it. **Unsupervised learning** searches for patterns without supplied target labels, for example grouping similar samples. **Reinforcement learning** learns actions using rewards from an environment. You only need these distinctions as vocabulary here; detailed algorithms from last semester are not prerequisites for this course.