Deep Learning · TEE · section

Classification regression and learning types

Neural-network foundations

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.

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.

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.