Supervised learning is a machine learning paradigm where a model is trained on a labeled dataset, meaning that each training example is paired with an output label. The goal is for the model to learn a mapping from inputs to outputs, enabling it to make accurate predictions on new, unseen data.

Supervised Learning

Supervised learning

The machine learning paradigm encompasses various approaches and methodologies that enable systems to learn from data and improve over time without explicit programming. It is categorized primarily into supervised, unsupervised, and reinforcement learning, each serving distinct applications and problem-solving strategies.

machine learning paradigm

labeled dataset

training example

output label

Model training is the process of teaching a machine learning algorithm to make predictions or decisions based on data. It involves optimizing the model's parameters to minimize error and improve accuracy using a training dataset.

Model Training

mapping from inputs to outputs

accurate predictions

New unseen data

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