Definition: Supervised learning is a type of machine learning where the algorithm is trained on a dataset with labeled examples.

Detailed Description:

In supervised learning, the algorithm learns a mapping function between input features and output labels. This allows the model to predict the output for new, unseen data.

Common supervised learning tasks include:

• Classification: Predicting a categorical label (e.g., spam or not spam, positive or negative sentiment).
• Regression: Predicting a continuous numerical value (e.g., house prices, stock prices).

Supervised learning algorithms include:

• Decision trees
• Support vector machines
• Neural networks
• Linear regression
• Logistic regression

Related Terms:

• Machine Learning
• Classification
• Regression
• Training Data
• Test Data

FAQs:

1. What is the difference between supervised and unsupervised learning?

   • In supervised learning, the algorithm is provided with labeled data, while in unsupervised learning, the algorithm is not given any labels.

2. What are some common applications of supervised learning?

   • Spam filtering, image recognition, medical diagnosis, and customer churn prediction.

3. What are the challenges of supervised learning?

   • Obtaining labeled data, dealing with imbalanced datasets, and preventing overfitting.