Predictive and Specialized Modeling
Introduction to Predictive and Specialized Modeling
Overview of Modeling Techniques
Predictive and Specialized Modeling provides details about more technical modeling techniques, such as Response Screening, Partitioning, and Neural Networks.
• The Neural platform implements a fully connected multi-layer perceptron with one or two layers. Use neural networks to predict one or more response variables using a flexible function of the input variables. See Neural Networks.
• The Partition platform recursively partitions data according to a relationship between the X and Y values, creating a decision tree of partitions. See Partition Models.
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The Bootstrap Forest platform enables you to fit an ensemble model by averaging many decision trees each of which is fit to a random subset of the training data. See Bootstrap Forest.
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The Boosted Tree platform produces an additive decision tree model that consists of many smaller decision trees that are constructed in layers. The tree in each layer consists of a small number of splits, typically five or fewer. Each layer is fit using the recursive fitting methodology. See Boosted Tree.
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The K Nearest Neighbors platform predicts a response value for a given observation using the responses of the observations in that observation’s local neighborhood. It can be used with a categorical response for classification and with a continuous response for prediction. See K Nearest Neighbors.
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The Naive Bayes platform classifies observations into groups that are defined by the levels of a categorical response variable. The variables (or factors) that are used for classification are often called features in the data mining literature. See Naive Bayes.
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The Support Vector Machines platform classifies observations into groups that are defined by levels of a categorical response variable. The model classifies data by optimizing a hyperplane that separates the classes. See Support Vector Machines.
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The Model Screening platform enables you to quickly run multiple predictive models and compare the results. Measures of fit are provided for each model along with overlaid diagnostic plots. See Model Screening.
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The Model Comparison platform enables you to compare the predictive ability of different models. Measures of fit are provided for each model along with overlaid diagnostic plots. See Model Comparison.
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The Make Validation Column platform lets you partition the data into two or three sets, using one of five different methods to create these partitions. See Make Validation Column.
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The Formula Depot platform enables you to organize, compare, profile, and score models for deployment. For model exploration work, you can use the Formula Depot to store candidate models outside of your JMP data table. See Formula Depot.
• The Fit Curve platform provides predefined models, such as polynomial, logistic, Gompertz, exponential, peak, and pharmacokinetic models. Compare different groups or subjects using a variety of analytical and graphical techniques. See Fit Curve.
• The Nonlinear platform lets you fit custom nonlinear models, which include a model formula and parameters to be estimated. See Nonlinear Regression.
• The Gaussian Process platform models the relationship between a continuous response and one or more continuous predictors. These models are common in areas like computer simulation experiments, such as the output of finite element codes, and they often perfectly interpolate the data. See Gaussian Process.
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The Functional Data Explorer platform enables you to convert functional data into a form that can be analyzed in another JMP platform. See Functional Data Explorer.
• The Time Series platform lets you explore, analyze, and forecast univariate time series. See Time Series Analysis.
• The Time Series Forecast platform lets you model and forecast multiple time series. The best fitting model is automatically selected from a set of up to 30 exponential smoothing models. See Time Series Forecast.
• The Matched Pairs platform compares the means between two or more correlated variables and assesses the differences. See Matched Pairs Analysis.
• The Explore Outliers platform enables you to identify, explore, and manage outliers in both univariate and multivariate data. See Explore Outliers.
• The Explore Missing Values platform provides several ways to identify, understand, and impute missing values in your data. See Explore Missing Values.
• The Explore Patterns platform is used to detect unusual or unexpected patterns in data. See Explore Patterns.
• The Response Screening platform automates the process of conducting tests across a large number of responses. Your test results and summary statistics are presented in data tables, rather than reports, to enable data exploration. See Response Screening.
• The Predictor Screening platform enables you to screen a data set for significant predictors. See Predictor Screening.
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The Association Analysis platform enables you to identify items that have an affinity for each other. It is frequently used to analyze transactional data (also called market baskets) to identify items that often appear together in transactions. See Association Analysis.
• The Process History Explorer platform enables you to identify problem components in complex process histories. See Process History Explorer.