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Advanced predictive modelers who use SAS Enterprise MinerStruktura kurzu
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SAS Enterprise Miner Prediction Fundamentals
- SAS Enterprise Miner prediction setup.
- Prediction basics.
- Constructing a decision tree predictive model.
- Running the regression node.
- Training a neural network.
- Comparing models with summary statistics.
Advanced Methods for Unsupervised Dimension Reduction
- Describe principal component analysis.
- Describe variable clustering.
Advanced Methods for Interval Variable Selection
- Explain how to use partial least squares regression in SAS Enterprise Miner.
- Using LAR/LASSO for variable selection.
Advanced Methods for Nominal Variable Selection and Model Assessment
- Implementing categorical input recoding.
- Creating empirical logit plots.
- Implementing all subsets regression.
Advanced Predictive Models
- Describe the basics of support vector machines.
- Using the HP Forest node in SAS Enterprise Miner to fit a forest model.
- Modeling rare events.
- Using the Rule Induction node in SAS Enterprise Miner.
Multiple Target Prediction
- Appraising model performance.
- Defining a generalized profit matrix.
- Creating generalized assessment plots.
- Using the Two-Stage Model node.
- Constructing component models.
Tips and Tricks with SAS Enterprise Miner
- Using the Open Source Integration node.
- Reusing metadata.
- Importing and using external models (self-study).
