1.Popular Features

• Model Fitting, Tuning, and Scoring
• Build, tune, and score datasets using over 50 algorithms across popular model families

2.Survival Analysis

Competing Risks, Kaplan-Meier estimation, Cox Proportional Hazards Model

3.Machine Learning

• Traditional Machine Learning Algorithms like decision trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), linear regression, and logistic regression, among others
• Deep learning: Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)

4.Six Sigma

• The functionality has been designed around the DMAIC methodology of Six Sigma, which is a data-driven quality strategy used to improve processes. DMAIC implies five quality improvement steps - Define, Measure, Analyze, Improve and Control. The comprehensive functionality includes
• Pareto charts, fishbone cause and effect diagrams, process capability analysis, measure system analysis (gage R&R analysis, attribute agreement, and gage bias analysis), seven types of Shewhart control charts (XBar-R, XBar-S, I-MR, I-MR Between/Within, p, np, u, c-charts) with customizable settings for testing eight special causes, the ability to label process stages/phases, T-squared/ MPCC charts, EWMA charts, CUSUM charts, and multi-vari charts
• Any number of additional sigma/limit lines can be added to any of the Shewhart charts to help identify early shifts and drifts in the process data