로지스틱 회귀
A type of regression model that predicts a probability. Logistic regression models have the following characteristics:1
- 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 model that predict probabilities for categorical labels.
A logistic regression model uses the following two-step architecture:1
- The model generates a raw prediction () by applying a linear function of input features.
- 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:1
- 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.