Learning Model
Jump to navigation
Jump to search
A Learning Model is a mathematical model that is trained on data to make predictions or decisions based on new inputs.
- Context:
- It can (typically) be used in various fields such as healthcare, finance, technology, and more.
- It can (often) be classified Learning Model Categories (such as supervised learning model).
- It can range from being a Simple Learning Model to being a Complex Learning Model.
- It can improve its performance by learning from additional data over time.
- It can require significant computational resources for training and inference.
- It can be evaluated using metrics such as accuracy, precision, recall, and F1 score.
- ...
- Example(s):
- a Linear Regression Model that predicts housing prices based on historical data.
- a Convolutional Neural Network (CNN) used for image recognition tasks.
- a K-Means Clustering Model that groups similar data points together.
- ...
- Counter-Example(s):
- Heuristic Algorithms, which do not learn from data but follow predefined rules.
- Rule-Based Systems, which rely on explicit instructions rather than data-driven learning.
- See: Supervised Learning Task, Unsupervised Learning Task, Reinforcement Learning Task, Machine Learning, Artificial Intelligence, Neural Network, Decision Tree, Support Vector Machine