Sample Complexity

Sample complexity refers to the amount of data needed for a machine learning model to learn effectively. It indicates how many examples or observations are required for the model to achieve a desired level of accuracy or performance. Generally, more complex tasks or models demand larger datasets to generalize well and avoid errors. Understanding sample complexity helps in designing efficient data collection strategies and setting realistic expectations about what can be learned from a given amount of data. Essentially, it quantifies the relationship between data quantity and learning quality.