Supervised Learning

AI Terms

Supervised Learning

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:

FAQs:

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.
What are some common applications of supervised learning?
- Spam filtering, image recognition, medical diagnosis, and customer churn prediction.
What are the challenges of supervised learning?
- Obtaining labeled data, dealing with imbalanced datasets, and preventing overfitting.

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