KnowledgeSTUDIO builds upon the market-leading data analysis and predictive analytics capabilities included in KnowledgeSEEKER with many advanced modeling and predictive analytics features for high-performance business users and quantitative analysts.
- Advanced modeling capabilities include linear and logistic regression, neural networks and scorecards (with reject inference).
- Unsupervised learning techniques include cluster analysis and principal component analysis.
KnowledgeSTUDIO delivers advanced business intelligence that translates into improved Return on Investment (ROI) through:
- Scorecards – Put the power of scorecard creation in the hands of internal staff. Whether to analyze credit risk or apply scorecard principles in non-credit risk domains, KnowledgeSTUDIO offers the ability to create predictive scorecards that span all customer-centric business processes—from acquisition to collection.
- Scoring – Assess and validate predictive models with an integrated set of scoring and deployment tools. Intuitive sampling and validation features include wizard-driven partitioning and hold-out options; Lift and Kolmogorov-Smirnov (K-S) charts; Relative Operating Characteristic (ROC); Goodness-of-Fit and Profit curves; Bias, Accuracy and Error charts; and Scatter plots.
- Text Analytics – Text Analytics optionally provides text and sentiment analysis by the embedded Salience Engine by Lexalytics®, the leading text and sentiment analysis engine provider.
- Code Generation – Analysts can quickly generate model code that can be exported into virtually any application, database or deployment system with automated SQL, XML, PMML and SAS generators.
- Hadoop Integration – Hadoop can be used with KnowledgeSTUDIO for data import and as a deployment platform for models created in KnowledgeSTUDIO.
KnowledgeSTUDIO can be deployed either on-premise for enterprise users or on-demand in the Cloud for small teams, short-term or project based initiatives.
|KnowledgeSTUDIO||Advanced Feature Highlights|
|Predictive Models||Linear and logistic regression and neural networks|
|Cluster Analysis||K-means and Expectation Maximization algorithms|
|Scorecards||Based on logistic regressions models; reject inference methods for credit application scorecards|
|Principal Component Analysis||To enable variable set reduction|
|Market Basket Analysis||Discovering association rules|
|Strategy Trees||Strategy design based on the decision tree graphical user interface (GUI)|
|Model Validation||Model performance and sensitivity evaluation, comparative model evaluation with cumulative lift and many others|
|Model Deployment||Both direct deployment and automatic generation of SAS, SQL, SPSS, PMML, and Java code for decision trees and strategy trees|
|Process Workflow||Process Map provides a visual audit trail of the analytical process workflow on a project basis|