What is a multitask?
A machine learning technique in which a single model is trained to perform multiple tasks.
multitask explained in plain English
A machine learning technique in which a single model is trained to perform multiple tasks. Multitask models are created by training on data that is appropriate for each of the different tasks. This allows the model to learn to share information across the tasks, which helps the model learn more effectively. A model trained for multiple tasks often has improved generalization abilities and can be more robust at handling different types of data.
Example
Practitioners refer to multitask when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
People also read
- A/B testing
A statistical way of comparing two (or more) techniques—the A and the B.
- ablation
A technique for evaluating the importance of a feature or component by temporarily removing it from a model.
- accuracy
The number of correct classification predictions divided by the total number of predictions.
- activation function
A function that enables neural networks to learn nonlinear (complex) relationships between features and the label.
- active learning
A training approach in which the algorithm chooses some of the data it learns from.
- adaptation
Synonym for tuning or fine-tuning.
- agglomerative clustering
See hierarchical clustering.
- anomaly detection
The process of identifying outliers.
- area under the PR curve
See PR AUC (Area under the PR Curve).
- area under the ROC curve
See AUC (Area under the ROC curve).