I wanted to score a model using SAS Eminer. But I'm not certain about the values to understand whether the model has a good score.
Appreciate if you could shed some light on this.
You can look at the results of the modeling node or use a Model Comparison node to see how well that model fits the training data (and validation/test data if you have those partitions). Then if you connect a score data set to the Score node along with the model you want to score with, then you will get new column(s) containing the predictions using that model. But I'm not sure if I'm answering your question?
Do you mean you want to assess a model where the target is known, or you want to score new data to create predictions where you don't have a target?
You can look at the results of the modeling node or use a Model Comparison node to see how well that model fits the training data (and validation/test data if you have those partitions). Then if you connect a score data set to the Score node along with the model you want to score with, then you will get new column(s) containing the predictions using that model. But I'm not sure if I'm answering your question?
For most of the Fit Statistics (misclassification rate, anything with "error" in it), smaller is better. In ROC and Lift plots, higher is better.
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Use this tutorial as a handy guide to weigh the pros and cons of these commonly used machine learning algorithms.
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