BASIC SUPERVISED CLASSIFICATION TASK | DEEP LEARNING AND ITS APPLICATION | SNS INSTITUTIONS

A basic supervised classification task is a type of machine learning problem where the goal is to assign an input to one of several predefined classes using labeled data. In supervised learning, each training example consists of input features and a known output label. The process begins with collecting a labeled dataset, where each data point is tagged with its correct class. The dataset is divided into training and testing sets. During training, a classification model such as logistic regression, k-nearest neighbors, support vector machine, or a neural network learns the relationship between input features and output labels. The model makes predictions by applying a decision rule or probability function to the input data. The difference between the predicted label and the actual label is calculated using a loss function, and the model parameters are updated to minimize this error. After training, the model is evaluated using metrics such as accuracy, precision, recall, and F1-score. Basic supervised classification tasks are widely used in real-world applications like spam detection, disease diagnosis, sentiment analysis, and image classification.
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