Machine Learning Algorithms diagram. This is one of the top business frameworks helping clients improve on their approach to strategy, project management, IT, HR, internal processes and client experience.All three techniques are used in this list of 10 common Machine Learning Algorithms: 1. Linear Regression To understand the working functionality of this algorithm, imagine how you would arrange random logs of wood in increasing order of their weight. There is a catch; however – you cannot weigh each log.
The problems in Machine Learning Algorithms could be divided into – Regression – There is a continuous relationship between the dependent and the independent variables. The target variable is numeric in nature, while the independent variables could be numeric or categorical.
Linear algorithms like Linear Regression, Logistic Regression are generally used when there is a linear relationship between the feature and the target variable, whereas the data exhibits non-linear patterns, the tree-based methods such as Decision Tree, Random Forest, Gradient Boosting, etc., are preferred.