mallioppimisen
Mallioppiminen, often translated as "model learning" or "example-based learning," is a fundamental concept in machine learning. It refers to the process where an algorithm learns to perform a task by analyzing a dataset of examples, where each example consists of an input and its corresponding desired output. Instead of being explicitly programmed with rules, the machine learning model infers patterns and relationships from this "training data."
The core idea is to train a model to recognize or predict a certain outcome based on
The effectiveness of mallioppiminen heavily relies on the quality and quantity of the training data. A diverse