OPTIMIZING LOGISTIC CLASSIFIER | DEEP LEARNING AND ITS APPLICATION | SNS INSTITUTIONS
Автор: M.Yogadharani SNS
Загружено: 2026-01-12
Просмотров: 3
A logistic classifier is used in binary classification problems, where the output belongs to one of two classes. It uses the sigmoid function to convert a linear combination of input features into a probability value between 0 and 1. To make accurate predictions, the logistic classifier must learn the optimal weights and bias. This is achieved by minimizing a loss function, commonly the log loss (cross-entropy loss), which measures the difference between predicted probabilities and actual class labels. Gradient descent is an optimization algorithm used to minimize this loss function. It works by computing the gradient of the loss with respect to each model parameter. These gradients indicate the direction in which the parameters should be adjusted to reduce the error. During each iteration, the weights and bias are updated in the opposite direction of the gradient using a learning rate. This process is repeated until the loss converges to a minimum. By using gradient descent, the logistic classifier gradually improves its performance and achieves better classification accuracy.
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