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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