CRISP-DM Process for Data Mining Quiz

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Questions and Answers

What is the Cross Industry Standard Process for Data Mining?

  • A process for data cleaning
  • A process for data mining (correct)
  • A process for data collection
  • A process for data analysis

What is important for understanding the objective of the problem, the subject area of the problem, and the data?

  • Model building
  • Data preparation
  • Prior knowledge (correct)
  • Knowledge and actions

What is the first step in the CRISP DM process?

  • Data preparation
  • Model building and evaluation
  • Test data creation
  • Prior knowledge (correct)

What is done during data preparation?

<p>Data exploration, data quality checks, handling missing values, data type conversion, transformation, and outliers (B)</p> Signup and view all the answers

What is used to evaluate the model?

<p>Test data (D)</p> Signup and view all the answers

What is done after the model is built?

<p>Knowledge and actions (C)</p> Signup and view all the answers

What is important for continuing the CRISP DM process after the model is built?

<p>Training data, testing the model, and applying the model to new data (D)</p> Signup and view all the answers

What is done using algorithms?

<p>Model building and evaluation (B)</p> Signup and view all the answers

What is used to evaluate the model?

<p>Test data (D)</p> Signup and view all the answers

What is the CRISP DM process repeated for?

<p>Different types of data (A)</p> Signup and view all the answers

Flashcards

CRISP-DM

A structured approach to data mining, ensuring comprehensive analysis and actionable insights.

Prior Knowledge

Understanding the core goal, context and information available.

Data Preparation

Exploring data, checking its quality, handling missing values, converting data types, transforming data, and managing outliers as the foundation.

Test Data

A dataset set aside to measure how well the model performs on unseen data.

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Knowledge and Action

Gaining insights and taking strategic actions based on the data mining results.

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Training Data

Datasets uses to train the model to identify patterns.

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Model Building and Evaluation

Using computational methods to construct and assess models.

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Data Variety in CRISP-DM

Different datasets may require unique adaptations of the CRISP-DM process to suit varying characteristics and challenges.

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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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