Training-validation-testing data is a set of data fed to a machine learning model to create the model and teach it how to accurately perform a specific task. The training data consists of input data and various annotations related to the task, as extracted from multiple sources. It can be raw data in the form of images, text, or sound, with labels such as ‘bounding boxes’, tags, or connections. The machine learning algorithms use the annotations in the training data to apply the same to new, unlabeled examples.
Applications of Training Data in Real-Life Situations
Training data is used for
- Developing deep learning models for applications such as facial recognition, object detection, and gesture recognition.
- Creating language models for speech recognition, text classification, and sentiment analysis.
- Training data for autonomous vehicles to enable image and video recognition from cameras, LiDAR, and other sensors.
- Modeling predictive analytics for forecasting, customer segmentation, and churn prediction with historical data.
- Implementing models for fraud detection applications such as credit card fraud, insurance fraud, and identity theft.
