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This topic explains how to visualize and interpret prediction results in Azure Machine Learning Studio (classic). After you have trained a model and done predictions on top of it ("scored the model"), you need to understand and interpret the prediction result.
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There are four major kinds of machine learning models in Azure Machine Learning Studio (classic):
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* Classification
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*[Assign to Clusters][assign-to-clusters] module for clustering
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*[Score Matchbox Recommender][score-matchbox-recommender] for recommendation systems
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This document explains how to interpret prediction results for each of these modules. For an overview of these modules, see [How to choose parameters to optimize your algorithms in Azure Machine Learning Studio (classic)](algorithm-parameters-optimize.md).
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Learn how to [choose parameters to optimize your algorithms in ML Studio (classic)](algorithm-parameters-optimize.md).
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This topic addresses prediction interpretation but not model evaluation. For more information about how to evaluate your model, see [How to evaluate model performance in Azure Machine Learning Studio (classic)](evaluate-model-performance.md).
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To learn how to evaluate your models, see [How to evaluate model performance](evaluate-model-performance.md).
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If you are new to Azure Machine Learning Studio (classic) and need help creating a simple experiment to get started, see [Create a simple experiment in Azure Machine Learning Studio (classic)](create-experiment.md).
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If you are new to ML Studio (classic), [learn how to create a simple experiment](create-experiment.md).
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## Classification
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There are two subcategories of classification problems:
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