Classification topics covered in this story are:

· What is Classification?

· Why do we need classification?

· Classification terminologies

· Types of Classification Algorithms

· Performance measure for classification algorithm

· Algorithm Selection

1-> What is Classification?

Two of the most Supervised learning algorithm tasks are Regression (predicting some value) and Classification (Predicting Class). It can be performed on both structure and unstructured data. The process start with predicting the class are often referred to as target, label or category.

In machine learning, classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. Examples of classification problems include, classify if email is spam or not. Given a handwritten character, classify it as one of the known characters.

Email or Spam

Classification is a process of categorizing a given set of data into classes, It can be performed on both structured or unstructured data. The process starts with predicting the class of given data points. The classes are often referred to as target, label or categories.

In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.

#classification #k-fold #logistic-regression #crossvalidation #confusion-matrix