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