Decision Tree Split Methods
Decision tree is one of the most popular supervised machine learning algorithms used for classification as well as regression problems.
As the name suggest decision tree is a tree like model which is built upside down with its root node at the top. Root node splits into different branches, the branch end that doesn’t split anymore is the leaf node or terminal node. Each root represents a feature, each branch represents decision and each leaf represents an outcome.
A beginner’s guide to Clustering Algorithm
As the name suggests, it involves dividing the data points into groups and each group consist of similar data points. In theory, data points that are in the same group should have similar properties, while data points in different groups should have highly dissimilar properties. Clustering is an unsupervised learning problem, it deals with finding a structure in collection of unlabeled data.
The purpose of clustering is to make sense of and extract values from large sets of structured and unstructured data. It helps you to glance through the data to pull out some…