Databricks Certified Machine Learning - Associate
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In the context of model interpretability using Spark ML, explain the process of understanding the importance of features and the model's predictions. Provide a code snippet demonstrating the use of Spark ML's TreeModel or TreeExplainer for model interpretability and explain the key considerations to keep in mind during this process.
In the context of model interpretability using Spark ML, explain the process of understanding the importance of features and the model's predictions. Provide a code snippet demonstrating the use of Spark ML's TreeModel or TreeExplainer for model interpretability and explain the key considerations to keep in mind during this process.
Simulated
A
Use the TreeModel class from the pyspark.ml.tree module to obtain the feature importances and prediction details of a decision tree model.
4.8%
B
Use the TreeExplainer class from the pyspark.ml.explainers module to explain the predictions of a tree-based model by showing the contribution of each feature to the prediction.
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