on the representation and the evaluation aspects optimization methods can be methods like gradient descent and even meta-heuristical methods like generic algorithms the three stages of logistic regression the best way to understand the nuances of complex process is to explain it using an example in this sectionwe trace the three stages of the machine learning processusing the logistic regression model logistic regression is an extension of linear regression to solve classification problems we will see how simple logistic regression problem is solved using gradient descent based optimizationwhich is one of the most popular optimization methods representation the representation of logistic regression is obtained by applying the logit function to the representation of linear regression model the linear regression representation is given by this hypothesis functionh(ththt hereth represents the parameters of the model and is the input vector the logit function is given bys ( - applying the logit function on the representation of linear regression gives us the representation of logistic regression ( - this is our representation for the logistic regression model as the value of logit function ranges between and we can decide between the two categories by supplying an input vector and set of parameters th and calculating the value of (thif it is less than then typically the label is otherwisethe label is (binary classification problems leverage thisevaluation the next step in the process is specifying an evaluation or cost function the cost function in our case is dependent on the actual class of the data point suppose the output of the logit function is for data point whose class is then the error or loss of that case is but if that data point is of category then the error is using this analogywe can define the cost function for one data point as follows cost hq log(( - hq ))if = log if = leveraging the previous logicthe cost function for the whole dataset is given bym cost ( ( log ( log =