Explain supervised machine learning algorithms (support vector machine, penalized regression, k-nearest neighbor, random forest, classification and regression tree, and ensemble learning) and decide which kinds of problem each handles best

Penalised regression such as LASSO adds λ times the sum of the absolute coefficients to the sum of squared residuals, shrinking weak coefficients to zero, which suits data sets with many features. A support vector machine finds the boundary with the widest margin between two classes and suits high-dimensional classification. k-nearest neighbour classifies a new observation by majority vote of its k closest observations, so the choice of k and of distance measure matters. Classification and regression trees split data by the feature and cut-off that best separate the target, and are easy to interpret but prone to overfitting. Ensemble learning combines many models; a random forest averages trees built on bootstrap samples and random feature subsets.