Neural network modeling represents the response variable as a linear function of the predictor variables.
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Q15: The confusion matrix helps you assess your
Q16: Linear regression requires the assumptions of independence,
Q17: If the outcome variable is quantitative and
Q18: To avoid overfitting in the neural network
Q19: The AIC never penalizes the model for
Q21: In logistic regression, _ is used as
Q22: Neural networks are common for large data
Q23: After calculating the squared differences in linear
Q24: In linear discriminate analysis we calculate the
Q25: In logistic regression,
A) the dependent variable is
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