Unlike linear regression, wherein we predicted continuous values for the outcome (the y variable), logistic regression and the Support Vector Machine (SVM) are used to predict just one out of the n possibilities for the outcome (the y variable). If the outcome is one of two possibilities, then the classification is called a binary classification.
Logistic regression, when used for binary classification, looks at each data point and estimates the probability of that data point falling under the positive case. If the probability is less than a threshold, then the outcome is negative (or 0); otherwise, the outcome is positive (or 1).
As with any other supervised learning techniques, we will be providing training examples for logistic regression. We then add a bit of code for feature extraction and let the algorithm create a model that encapsulates the probability of each of the features belonging to one of the binary outcomes.
What SVM tries...