Multitask Learning (MTL) is a machine learning approach where a single model is trained to perform multiple tasks simultaneously. This method leverages shared information between tasks, allowing the model to learn more effectively and achieve better performance compared to training separate models for each task. By utilizing a shared representation, MTL can enhance generalization and reduce the risk of overfitting, as the model learns from the domain information contained in the training signals of related tasks ².

In practice, MTL is particularly useful in scenarios where tasks are related, such as in recommendation systems or search engines, where user satisfaction can be measured through multiple metrics. By optimizing for these various metrics at once, MTL can lead to improved outcomes ². Additionally, in libraries like Sklearn, multitask classification can be implemented using classifiers that support multiple outputs, such as RandomForestClassifier or GradientBoostingClassifier ⁵.

If you have more specific questions about MTL or its applications, feel free to ask!