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This last alternative is logistic regression. Formally, the model logistic regression model is that log p(x) 1− p(x) =β 0 +x ·β (12.4) Solving for p, this gives p(x;b,w)= e β 0+x· 1+eβ 0+x·β = 1 1+e−(β 0+x·β) (12.5) Noticethattheover-allspecificationisaloteasiertograspintermsofthetransformed probability that in terms of the untransformed probability.1 Logistic regression is a kind of statistical analysis that is used to predict the outcome of a dependent variable based on prior observations. For example, an algorithm could determine the winner of a presidential election based on past election results and economic data. Logistic regression algorithms are popular in machine learning. Logistic Regression. Learning Objectives Rationale for Logistic Regression Identify the types of variables used for dependent and independent variables in the application of logistic regression Describe the method used to transform binary measures into the likelihood and probability measures used in logistic regression Interpret the results of a logistic regression analysis & assessing ...