Satisfies study design dot-point “explain, at a high level, how data-driven algorithms can learn from data”
Machine learning models can be ‘trained’
Data-driven algorithms, such as machine learning models, learn from data by finding and recognising patterns.
Instead of being explicitly programmed with rules, algorithms ‘train’ on a large dataset contained with input data and, depending on their type of learning, their corresponding correct output:
- supervised learning: the algorithm is given labelled data, with each input having a correct answer. (e.g. email contents, and whether the email is spam)
- unsupervised learning: the algorithm is given unlabelled data with no correct answers. The algorithm then attempts to find hidden groupings on its own.
- reinforcement learning: the algorithm learns through trial and error.
Once training is complete, the algorithm has ‘learned’ patterns from the given data. When given new, unseen data, it applies what it learned to make a prediction. The model’s success is often determined by how accurately it can generalise its knowledge, and the quality of the input data.