Overview
Supervised learning is a foundational concept in machine learning that involves training models on labeled datasets. By learning from input-output pairs, these models can make predictions on new, unseen data. This approach is widely used in various applications, such as email filtering, image recogn...
Key Terms
Example: In a dataset of emails, labeled data would indicate which emails are spam and which are not.
Example: 80% of the dataset is often used as a training set.
Example: The remaining 20% of the dataset is used as a test set.
Example: A model that performs well on training data but poorly on test data is likely overfitting.
Example: If a model predicts 90 out of 100 instances correctly, its accuracy is 90%.
Example: If a model predicts 10 emails as spam and 8 are actually spam, its precision is 80%.