Term
Supervised Learning
Definition
Supervised learning is a technique where artificial intelligence (AI) systems learn using data that already has answers to effectively predict or identify similar data in the future.
Where you’ll find it
In AI platforms, supervised learning is used within the training modules to train AI models. It is essential for applications that require accurate predictions based on past data.
Common use cases
- Predicting customer behavior based on past purchases.
- Identifying objects in images, such as faces or gestures.
- Filtering emails into 'spam' or 'not spam' categories.
Things to watch out for
- Ensure that the labeled data used is diverse and reflective of all possible scenarios to avoid biased AI models.
- Regular updates of your data set are necessary to account for new trends and changes.
- Overfitting occurs when a model is so finely tuned to the training data that it performs poorly on any new data.
Related terms
- Predictive Analytics
- Classification
- Labeling
- Training Data
- Neural Networks
Pixelhaze Tip: Always check the balance and variety in your training datasets. A well-rounded dataset helps in building models that perform well in real-world situations. Adjusting and refining the data can significantly improve the prediction accuracy of your supervised learning model.