Description

Training data is the foundation upon which machine learning models are built. It consists of a collection of examples that the model uses to learn patterns, relationships, and rules relevant to the task it's being trained for. In supervised learning, training data includes both input features and corresponding output labels. For unsupervised learning, it may consist of unlabeled data from which the model must discover inherent structures. The quality, quantity, and diversity of training data significantly impact a model's performance and generalization ability.

Examples

• 🖼️ Labeled images for image classification
• 📈 Historical stock prices for financial forecasting
• 💬 Conversational logs for chatbot training

Applications