RapidMiner Auto Model
Create predictive models in 4 clicks using automated machine learning and data science best practices
Guided Data Prep
Simplified point+click data prep for predictive modeling
- Automatically analyze data to identify common quality problems like correlations, missing values, and stability
- Remove problem data columns with a single click
- Further prep, blend, and wrangle data with RapidMiner Turbo Prep
Automated Model Selection and Tuning
Find the best model for your data using multiple machine learning algorithms and hyperparameter optimization
- Choose from popular classification, clustering, and outlier detection machine learning models
- Automated hyperparameter tuning finds the best parameters for the model
- Supports Native Bayes, Generalized Linear Model, Logistic Regression, Deep Learning, Decision Tree, Random Forest, Gradient Boosted Trees (XGBoost), Support Vector Machines, and kMeans Clustering
Automatic Feature Engineering
Automated feature engineering turns good models into exceptional models without sacrificing explainability
- Unique Evolutionary Feature Selection lets users choose between accurate, simple, and balanced models
- Automatically generate new features from data to improve model accuracy
- Create hundreds of thousands of models to uncover the best one
Turn Predictive Models into Prescriptive Actions
Make better business decisions using predictive analytics
- Use a simple drag+drop interface to learn how changes in the values of each feature affect model performance
- Export predictions to popular business applications like Excel and data visualization tools
No Black Boxes
Create predictive models that are easy to explain and easy to trust
- View the top variables for each model to see exactly how the model came to its conclusions
- The predictive model is documented and annotated step-by-step for complete transparency and trust
- Each step in the workflow process can be tuned and refined inside of the RapidMiner Studio visual workflow designer
