the simplest classification algorithm is logistic regressionwhich is also linear method but the predictions are transformed using the logistic function the outputs of this transformation are class probabilities--in other wordsthe probabilities that the instance belongs to the various classeswhere the sum of the probabilities for each instance adds up to one each instance is then assigned to the class for which it has the highest probability of belonging in strengths like linear regressionlogistic regression is simple and interpretable when the classes we are trying to predict are nonoverlapping and linearly separablelogistic regression is an excellent choice weaknesses when classes are not linearly separablelogistic regression will fail applications when classes are mostly nonoverlapping--for examplethe heights of young children versus the heights of adults--logistic regression will work well neighborhood-based methods another group of very simple algorithms are neighborhood-based methods neighborhood-based methods are lazy learners since they learn how to label new points based on the proximity of the new points to existing labeled points unlike linear regression or logistic regressionneighborhood-based models do not learn set model to predict labels for new pointsratherthese models predict labels for new points based purely on distance of new points to preexisting labeled points lazy learning is also referred to as instance-based learning or nonparametric methods -nearest neighbors the most common neighborhood-based method is -nearest neighbors (knnto label each new pointknn looks at number (where is an integer valueof nearest labeled points and has these already labeled neighbors vote on how to label the new point by defaultknn uses euclidean distance to measure what is closest the choice of is very important if is set to very low valueknn becomes very flexibledrawing highly nuanced boundaries and potentially overfitting the data if is set to very high valueknn becomes inflexibledrawing too rigid boundary and potentially underfitting the data strengths unlike linear methodsknn is highly flexible and adept at learning more complexnonlinear relationships yetknn remains simple and interpretable unsupervised learning in the machine learning ecosystem