10 Questions
What is the Cross Industry Standard Process for Data Mining?
A step-by-step process for data mining
What is important for understanding the objective of the problem, the subject area of the problem, and the data?
Prior knowledge
What is done to prepare the data before modeling?
Data quality checks
What is done to build and evaluate the model?
Algorithms
What must be created and used to evaluate the model?
Test data
What is repeated for different types of data?
The CRISP DM process
What is important for continuing the CRISP DM process after the model is built?
Training data
What is the first step of the CRISP DM process?
Prior knowledge
What is used to evaluate the model?
Test data
What is done to build and evaluate the model?
Algorithms
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.
Test your knowledge of the CRISP DM process for data mining, which involves understanding the problem, preparing the data, building and evaluating models, and applying the models to new data.
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