Supervised Learning
2024 | AI Dictionary
What is Supervised Learning: A type of machine learning where models are trained on labeled data pairs to learn mapping between inputs and correct outputs.
What is Supervised Learning?
Supervised Learning is a type of machine learning where the model is trained on labeled data, meaning the input data is paired with the correct output. The model learns to map inputs to the correct output during training and can then predict the output for unseen data.
Types of Supervised Learning
- Classification : The model predicts discrete labels or categories (e.g., identifying spam vs. non-spam emails).
- Regression: The model predicts continuous values (e.g., predicting house prices based on features like size and location).
Applications of Supervised Learning
- Email Spam Filtering: Using labeled emails to classify whether new emails are spam or not.
- Medical Diagnosis: Predicting diseases based on patient data such as symptoms and test results.
- Customer Churn Prediction: Identifying which customers are likely to stop using a service based on their behavior.
Example of Supervised Learning
In image classification, a supervised learning model can be trained on a dataset of labeled images (e.g., images of dogs and cats with labels “dog” or “cat”). The model learns to distinguish between the two categories, and after training, it can predict the label of new, unseen images.
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