LogisticRegression.md (1186B)
1 # Logistic Regression (Logit Regression) 2 3 ML D3 4 5 ## Notes 6 7 **Definition:** Logistic regression is a regression method used to determine the probability of some sample being part of some class. 8 9 These are often binary classifiers (when they don't output probabilities) as they can simply output 1 or 0 depending on which probability is higher. 10 11 Logistic regression works under the hood by computing a weighted sum of input features and then uses that as an input to a sigmoid function. The output of the sigmoid function is then the probability of it being in the class. The outputting of the output of the sigmoid function is called the logistic of the result. 12 13 An interesting thing about logistic regression is that the log loss function does not have a known closed form equation for gradient descent must be used to optimize the algorithm. 14 15 With the sigmoid function we define the decision boundary as the x-value for which greater values are true and lesser values are false. This position is at the 50% probability mark. 16 17 See [SoftmaxRegression](SoftmaxRegression.md) for an extrapolation of linear regression for multi-class classification without combining binary classifiers.