Buscar
Estás en modo de exploración. debe iniciar sesión para usar MEMORY

   Inicia sesión para empezar

level: Supervised Learning

Questions and Answers List

level questions: Supervised Learning

QuestionAnswer
SUPERVISED LEARNINGSupervised 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 LEARNINGClassification and regression.
CLASSIFICATION IN SUPERVISED LEARNINGThe 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 LEARNINGThe 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 ALGORITHMSLinear Classifiers, Support Vector, Machines Decision Trees, K-Nearest Neighbor, Random Forest
REGRESSION IN SUPERVISED LEARNINGRegression 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 ALGORITHMSLinear Regression, Logistic Regression, Polynomial Regression