Overview
Supervised learning algorithms are essential tools in machine learning, allowing models to learn from labeled data to make predictions. By understanding the relationship between input features and output labels, these algorithms can be applied to various real-world problems, such as spam detection a...
Key Terms
Example: A dataset of house prices with features like size and location.
Example: In a spam detection model, 'spam' or 'not spam' are labels.
Example: A model that performs well on training data but poorly on test data.
Example: If a model predicts 80 out of 100 correctly, its accuracy is 80%.
Example: If a model predicts 10 positives and 8 are correct, precision is 80%.
Example: If there are 10 actual positives and the model finds 8, recall is 80%.