Логистическая регрессия

A type of regression model that predicts a probability. Logistic regression models have the following characteristics: - The label is categorical. The term logistic regression usually refers to binary logistic regression , that is, to a model that calculates probabilities for labels with two possible values. A less common variant, multinomial logistic regression, calculates probabilities for labels with more than two possible values. - The loss function during training is Log Loss. (Multiple Log Loss units can be placed in parallel for labels with more than two possible values.) - The model has a linear architecture, not a deep neural network. However, the remainder of this definition also applies to deep models that predict probabilities for categorical labels. For example, consider a logistic regression model that calculates the probability of an input email being either spam or not spam. During inference, suppose the model predicts 0.72. Therefore, the model is estimating: - A 72% chance of the email being spam. - A 28% chance of the email not being spam. A logistic regression model uses the following two-step architecture: 1. The model generates a raw prediction (y') by applying a linear function of input features. 2. The model uses that raw prediction as input to a sigmoid function, which converts the raw prediction to a value between 0 and 1, exclusive. Like any regression model, a logistic regression model predicts a number. However, this number typically becomes part of a binary classification model as follows: - If the predicted number is greater than the classification threshold, the binary classification model predicts the positive class. - If the predicted number is less than the classification threshold, the binary classification model predicts the negative class. See Logistic regression in Machine Learning Crash Course for more information.