Training Data
The dataset used to train machine learning models. Training data teaches the model patterns and relationships it will apply to new, unseen data.
In-Depth Explanation
Training data is the foundation of machine learning. Models learn patterns from training examples, then generalise to make predictions on new data. Training data quality directly determines model quality.
Training data components:
- Features: Input variables (what the model sees)
- Labels: Target outputs (what the model predicts)
- Examples: Individual data points
Training data requirements:
- Representative: Covers the real-world distribution
- Sufficient quantity: Enough to learn patterns
- High quality: Accurate, complete, consistent
- Properly labelled: Correct ground truth
- Balanced: Adequate examples of all classes
Data splits:
- Training set: ~70-80% for learning
- Validation set: ~10-15% for tuning
- Test set: ~10-15% for final evaluation
Business Context
Training data is often the biggest investment in ML projects. Quality data is more valuable than sophisticated algorithms.
How Clever Ops Uses This
We help Australian businesses prepare training data for AI projects, ensuring quality and representativeness for their specific use cases.
Example Use Case
"Curating 10,000 labelled customer support tickets to train a classification model, ensuring balanced representation across categories."