Predictive Modeling and Machine Learning
Remove the guesswork. Don't leave the future to chance.
- Build better models with modern predictive modeling techniques, like regression, neural networks, and decision trees.
- Automatically fit multiple predictive models and determine the best-performing model with model screening.
- Avoid overfitting using cross-validation and K-fold cross-validation.
- Use machine learning methods without having to write code and tune algorithms.
With JMP, we can find the most effective way to slice up the data or show the results of a machine model without spending a lot of time making the program do something it wasn’t explicitly designed to do.
Greg Mattiussi
Senior Director of Manufacturing, Siemens Healthineers
Regression
- Multiple linear regression
- Logistic regression
- Generalized regression PRO
- Quantile regression PRO
- Penalized regression PRO
- Regularized regression PRO
- LASSO PRO
- Elastic net PRO
- Ridge PRO
Decision Trees
- Bootstrap forest PRO
- Boosted tree PRO
- Random forest PRO
- Gradient boosting PRO
- Partition
- Recursive partitioning
Other Predictive Models
- k-NN PRO
- Naïve Bayes PRO
- SVM PRO
- Discriminant
- Multiple linear regression
- Logistic regression
- Generalized regression PRO
- Quantile regression PRO
- Penalized regression PRO
- Regularized regression PRO
- LASSO PRO
- Elastic Net PRO
- Ridge PRO
- Bootstrap forest PRO
- Boosted tree PRO
- Random forest PRO
- Gradient boosting PRO
- Partition
- Recursive partitioning
Validation (Cross validation)
- K-fold validation PRO
- Data partitioning PRO
- Holdout PRO
- Holdback PRO
Model Selection
- Model screening PRO
- Model comparison PRO
- Confusion matrix
- Model averaging
- Ensemble
- Profit matrix
Model Deployment
- Scoring
- Scoring code
- Model management PRO
- Formula Depot PRO
- Prediction formula
Text Mining (Text Analysis)
- Latent class analysis PRO
- Latent semantic analysis PRO
- Sentiment analysis PRO
- Term selection PRO
- Text regression PRO
Featured Resources
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How to ensure your investment in Machine Learning yields beneficial outcomesIn this talk, Jonathan Williams, PhD, Data Analysis Manager at IQE, shares guidance and stories about how to get started with machine learning techniques or effectively integrate them within existing programs.
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Applying machine learning to the right problems Speakers from Procter & Gamble and TEL Singapore on machine learning and automation
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Discovering and Predicting Patterns Using Neural Network ModelsSee how to build neural networks, starting with a simple one-layer network, and how to use JMP Pro to build more complicated self-learning and boosted models.
JMP® Analytic Capabilities
See everything that JMP® can do for you and your organization, from data access and cleaning, to exploration and visualization, all the way through sharing and communicating your results.
