Classification requires discrete labels. Regression predicts points along a continuous scale. Logistic regression passes the output through a sigmoid function, mapping the output between 0 and 1. This is used for binary classification.

This is a binary classification task, therefore the sigmoid output of logistic regression (which maps outputs between 0 and 1) will suit well.

Is the following statement True or False? Gradient descent is a handy technique which allows us to use non differentiable functions/models and update them iteratively towards an optimum. If the function had been differentiable we could have simply found the exact solution to the problem by setting the derivative to 0 and rearrange to find the optimal parameters.

We need to be able to differentiate the function to know which direction in the parameter space to move/step. The reason why we use gradient descent rather than finding the exact solution is that finding the exact solution requires matrix inversion which has cubic complexity.

We can perform a series of transformations on our input x. For example:

Log loss is a measure of how close the model's predicted probability of a class is to the true value/label. ROC-AUC is a measure of the degree to which a model can distinguish between two classes. Accuracy is the degree to which the predicted classes match the true classes. Only mean squared error from these options can be applied to a regression task. Mean squared error is the expected distance between the models predictions and the true values.