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 related tasks, which can lead to improved performance compared to training separate models for each task. By learning tasks in parallel and using a shared representation, MTL helps in knowledge transfer, allowing what is learned for one task to benefit others ².

One of the key advantages of MTL is its ability to reduce the risk of overfitting. Since the model is exposed to multiple tasks, it can generalize better by utilizing the domain information contained in the training signals of related tasks. This inductive transfer enhances the model’s ability to learn from limited data ².

In practical applications, MTL is often used in complex systems like recommendation engines and search algorithms, where user satisfaction can be measured through various metrics simultaneously ². Additionally, in libraries like Sklearn, multitask classification allows a single model to predict multiple outputs for each input, further demonstrating the versatility of MTL in real-world scenarios ⁵.