SAS JMP COX PROPORTIONAL PRO
Large models should always be cross-validated, and JMP Pro does this through data partitioning, or holdback. For effective predictive modeling, you need sound ways to validate your model, and with a large model, you can easily get into trouble over-fitting. The platform automatically handles missing values and transformation of continuous X’s, which saves time and effort and includes robust fitting options.Įach of these platforms in JMP Pro uses cross-validation, which offers a way to validate your model and generalize well to tomorrow’s data. The advanced Neural platform lets you build one- or two-layer neural networks with your choice of three activation functions and also provides automatic model construction using gradient boosting. The platform even allows predictions for combinations of predictors that do not appear in your data. The Naive Bayes platform uses the principles of Bayes’ Theorem to allow you to predict a categorical response. The boosted tree technique builds many simple trees, repeatedly fitting any residual variation from one tree to the next. The bootstrap forest, which uses a random-forest technique, grows dozens of decision trees using random subsets of the data and averages the computed influence of each factor in these trees. This platform also fits K nearest neighbors (K-NN) models. The Partition platform in JMP Pro automates the tree-building process with modern methods. Some of the most useful techniques for predictive modeling are decision trees, bootstrap forest, Naive Bayes and neural networks.
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JMP Pro includes a rich set of algorithms for building better models of your data.
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But without the right tools and the most modern techniques, building a model to predict what will happen with new customers, new processes or new risks becomes much more difficult. Anyone can do a fair job of describing last year's performance.