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A model that has unknown sets of inputs producing unique outputs aligns closely with unsupervised or reinforcement learning in machine learning, depending on the context. In such cases, the system must discover patterns or optimal actions without explicit labeled guidance.
Machine Learning models are computational programs trained to identify patterns in data and make predictions or decisions on unseen inputs. They adapt over time, improving accuracy as more data or feedback is provided.
1. Unsupervised Learning When the input data is unlabeled and the relationships between inputs and outputs are unknown, the model must self-discover structure in the data.
Goal: Group, associate, or reduce data dimensions without predefined outcomes.
Common algorithms: K-Means, DBSCAN, Hierarchical Clustering, PCA, Gaussian Mixture Models.
Example: Segmenting customers into groups based on purchasing behavior without knowing the categories beforehand.
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