Classification is the process of predicting the class of given data points. Classes are referred to as targets/ labels or categories. Classification predictive modeling is the task of approximating a mapping function (f) from input variables (X) to discrete output variables. It is predictive modeling that predicts discrete variables – True and False, Yes and No, 1 and 0, and so on.
There are multiple applications of Classification in various domains such as BFSI, Medical, Telecom, and so on. Some of the common use cases are in loan approval, probability of a patient suffering a heart attack, spam mail detection, and so on.
Classification belongs to the category of supervised learning. There are two types of learning techniques in Classification – Lazy Learning and Eager Learning.
Lazy Learning Technique – In this, the training data is simply stored and the construction of the generalization model is deferred until testing data is received. The classification model is constructed only after arrival of the testing data.
This technique is characterized by less learning time; but more prediction time.
Some of the examples of Lazy learners are k Nearest Neighbor, Case-based reasoning, and so on.
Eager Learning Technique – In this, the classification model is constructed when the training data is received. The algorithm is said to be eager to create a model and does not wait for receipt of the testing data.
This technique is characterized by more training time and less prediction time.
Some of the examples of Eager learners are Decision Tree, Naïve Bayes, and so on.
Notes:

• The Reader (Dataset) should be connected to the algorithm.
• Missing values should not be present in any rows or columns of the reader. To find out missing values in a data, use Descriptive Statistics. Refer to Descriptive Statistics.
• If missing values are present, impute them using Missing Value Imputation on the dataset. It is under Model Studio, in Data Preparation. Refer to Missing Value Imputation.
• The dependent or output variable must be a discrete data type.

List of Classification Algorithms

• MLP Neural Network
• Adaboost
• Decision Tree
• Extreme Gradient Boost Classification
• Gradient Boosting
• K Nearest Neighbour
• Binomial Logistic Regression
• Multinomial Logistic Regression
• Naïve Bayes
• Random Forest
• Support Vector Machine