Introducing Skater.

Understand the decision-making processes of complex data models with Skater, a Python library for model interpretation from DataScience.com Labs.

Start Interpreting Complex Models In Python With Skater

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View The Skater Installation Guide, Tutorials, And Dependencies

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Learn How Model Interpretation Works And Why It Matters

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What is Skater?

Skater is Python library designed to demystify the inner workings of complex or black-box models. Skater uses a number of techniques, including partial dependence plots and local interpretable model agnostic explanation (LIME), to clarify the relationships between the data a model receives and the outputs it produces. With Skater, interpret models both before and after they are deployed into production.

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What is model interpretation?

Model interpretation is the process of understanding how a model makes a prediction based on the data it receives. With this information, you can establish trust in a model’s outputs, identify hidden feature interactions, and compare how different models make decisions. This process is especially useful in applications such as credit risk modeling, where a data scientist might have to explain why a model denied a customer a credit card.

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How does Skater improve modeling?

Often, practitioners will select simple modeling techniques — such as linear regression or decision trees — over more accurate neural networks and ensembles because those models are easier to interpret. Skater aims to eliminate this compromise by providing a common interpretation framework for all models, regardless of complexity or the algorithm, language, or library used to build them. 

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