JMP Learning Library
- Classification Trees (Partition)Build a partition based model (Decision Tree) that identify the most important factors that predict a categorical outcome and use the resulting tree to make predictions for new observations.
- Regression Trees (Partition)Build a partition based model (Decision Tree) that identify the most important factors that predict a continuous outcome and use the tree to make prediction for new observations.
- Discriminant AnalysisBuild a boundary based statistical model to predict a categorical outcome as a function of multiple continuous preditor variables.
- Support Vector Machines (Classification)Build a boundary based statistical model to predict a categorical outcome as a function of multiple predictor variables.
- Support Vector Regression Build a boundary based statistical model to predict a continuous outcome as a function of multiple predictor variables.
- K Nearest NeighborsUse an algorithm to predict a categorical or continuous outcome for new observations based upon the outcomes of similar observations (i.e., nearest neighbors).
- Neural NetworksBuild a network based model to describe the impact that multiple predictor variables have on an outcome and to make predictions of a categorical or continuous outcome.
- Naive BayesUse Bayes conditional probabilities to predict a categorical outcome for new observations based upon multiple predictor variables.
- Creating a Validation Column (Holdout Sample)Subset data into a training, validation, and test set to more accurately evaluate a model's predictive performance and avoid overfitting.
- Model Comparison and SelectionCompare and contrast the performance of competing models in order to choose the best.
- Text Mining - Describing Unstructured Text DataSummarize unstructured text data through word clouds and tables of frequently used words and phrases.
- Text Mining - Sentiment AnalysisAssign numerical scores to words and phrases to quantify the positive and negative sentiment expressed in text.
- Text Mining - Advanced Analysis MethodsAnalyze unstructured text data by finding patterns, similarity, and relationships (Latent Class Analysis, Latent Semantic Analysis, SVD).
- Association Analysis (Market Basket Analysis)Analyze transactional data to develop rules that estimate the likelihood of items/events occurring based on the occurrence of other items/events.