Model, The representation of patterns learned from data in supervised learning., Training, The process of teaching a machine learning model using labeled data., Labels, The known outputs or categories assigned to the input data used for training., Features, The measurable properties or characteristics used to predict the target variable., Accuracy, A metric measuring the correctness of predictions made by a model., Classification, A type of supervised learning where the goal is to categorize input into classes or categories., Regression, Another type of supervised learning focused on predicting continuous numerical values., Overfitting, When a model learns too much from the training data and performs poorly on new, unseen data., Underfitting, Occurs when a model is too simple to capture the patterns in the training data., Validation, The process of assessing a model's performance on data not used during training..

Supervised Learning

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