Logistic regression and neural networks use complex nonlinear functions to capture the relationship between explanatory variables and categorical dependent variables.

Question

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Logistic regression uses a sigmoid function, while neural networks use a variety of activation functions, such as the ReLU or tanh function. These functions are all nonlinear, which allows them to capture complex relationships between the explanatory variables and the categorical dependent variable.

Logistic Regression and Neural Networks Use Complex Nonlinear Functions to Capture