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Questions and Answers
What is the Cross Industry Standard Process for Data Mining?
What is important for understanding the objective of the problem, the subject area of the problem, and the data?
What is the first step in the CRISP DM process?
What is done during data preparation?
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What is used to evaluate the model?
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What is done after the model is built?
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What is important for continuing the CRISP DM process after the model is built?
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What is done using algorithms?
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What is used to evaluate the model?
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What is the CRISP DM process repeated for?
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Study Notes
- The Cross Industry Standard Process for Data Mining, or CRISP DM, is a step-by-step process for data mining.
- Prior knowledge is important for understanding the objective of the problem, the subject area of the problem, and the data.
- Data must be prepared before modeling can be done. This includes data exploration, data quality checks, handling missing values, data type conversion, transformation, and outliers.
- Model building and evaluation is done using algorithms.
- Test data must be created and used to evaluate the model.
- The CRISP DM process is repeated for different types of data.
- Knowledge and actions are important for continuing the CRISP DM process after the model is built. This includes training data, testing the model, and applying the model to new data.
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Description
Test your knowledge of the CRISP-DM process for data mining with this quiz. Explore topics such as data preparation, model building and evaluation, and the importance of prior knowledge and actions in the process.