| SUPERVISED LEARNING | Supervised learning (SL) is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. |
| SUBCATEGORIES OF SUPERVISED LEARNING | Classification and regression. |
| CLASSIFICATION IN SUPERVISED LEARNING | The job of a classification algorithm is to then take an input value and assign it a class, or category, that it fits into based on the training data provided. |
| EXAMPLE OF CLASSIFICATION IN SUPERVISED LEARNING | The most common example of classification is determining if an email is spam or not. With two classes to choose from (spam, or not spam), this problem is called a binary classification problem. The algorithm will be given training data with emails that are both spam and not spam. The model will find the features within the data that correlate to either class and create the mapping function mentioned earlier: Y=f(x). Then, when provided with an unseen email, the model will use this function to determine whether or not the email is spam. |
| POPULAR CLASSIFICATION ALGORITHMS | Linear Classifiers, Support Vector, Machines Decision Trees, K-Nearest Neighbor, Random Forest |
| REGRESSION IN SUPERVISED LEARNING | Regression is a predictive statistical process where the model attempts to find the important relationship between dependent and independent variables. The goal of a regression algorithm is to predict a continuous number such as sales, income, and test scores. |
| MOST COMMON TYPES OF REGRESSION ALGORITHMS | Linear Regression, Logistic Regression, Polynomial Regression |
