TESTING MODEL DAN CROSS VALIDATION TUTORIAL RAPID MINER STUDIO

Summary

In this video, the speaker demonstrates the process of testing a decision model using the decision 3 algorithm. They explain how to prepare data, apply the model for training and testing, and evaluate its accuracy using the 'Apply Model' and 'Performance' operators. The speaker highlights the importance of accuracy and precision in predictions, showcasing an initial result of 91.67%. They then introduce cross-validation to improve model accuracy, achieving a 95% accuracy rate. The video offers valuable insights into decision modeling and the significance of cross-validation for precise predictions.

Takeaways

• 😀 Assalamualaikum greeting and introduction to the testing model using the Decision Tree algorithm.
• 😀 The data used in this model testing consists of a list of passing scores that are filtered for the training and testing process.
• 😀 The first step involves using the 'apply model' operator to process and duplicate the data for training and testing purposes.
• 😀 The model uses the Decision Tree algorithm to predict outcomes such as graduation status (e.g., on-time or late).
• 😀 The 'performance' operator is used to evaluate the accuracy of the predictions made by the model.
• 😀 A test result of 91.67% accuracy is achieved, indicating a good level of prediction accuracy for the model.
• 😀 The accuracy of the model can increase or decrease depending on the amount and quality of data tested.
• 😀 Cross-validation is introduced as a method to improve prediction accuracy by repeating the testing process multiple times (e.g., 10 times).
• 😀 The use of cross-validation enhances the model's reliability, with an improved accuracy rate of 95%.
• 😀 After completing the testing process, the model and results are saved with a clear label, such as 'Testing Model' or 'Cross Validation'.
• 😀 The final output emphasizes the importance of cross-validation in achieving more accurate and consistent predictions compared to a single model test.

Q & A

What is the primary focus of the video script?

The primary focus of the video script is on testing a model using the Decision Tree algorithm, particularly for predicting graduation status based on certain variables like age and status.

What role does the 'apply model' operator play in the process?

The 'apply model' operator is used to duplicate data from the list of values and connect them for training and testing in the model-building process.

What happens when the data is processed and filtered?

Once the data is processed and filtered, it is duplicated for the training and testing phases, where the model will be trained using one part of the data and tested using another.

How is the accuracy of the model assessed in the script?

The accuracy of the model is assessed using the 'performance' operator, which evaluates how well the model performs in terms of its predictions, providing an accuracy percentage.

What does the 91.67% accuracy indicate in the testing phase?

The 91.67% accuracy indicates that the model’s predictions are correct about 91.67% of the time, with higher values suggesting better performance.

What is the significance of the Cross Validation process mentioned in the video?

Cross Validation is used to improve the model’s prediction accuracy by repeating the testing process multiple times (10 times in this case) to obtain an average result, helping to ensure the model’s reliability and precision.

What happens when Cross Validation is applied to the data?

When Cross Validation is applied, it duplicates the data automatically, tests the model multiple times, and calculates the average accuracy of predictions, leading to a more accurate and reliable result.

How does the configuration of the presentation impact prediction accuracy?

The configuration of the presentation affects the model’s ability to make accurate predictions. A higher configuration number results in better alignment between the predicted and actual results, whether it’s graduation status or other factors.

What is the purpose of the 'performance' operator in this process?

The 'performance' operator is used to measure and evaluate the model's accuracy, helping to determine how well it predicts the correct outcome, such as graduation status, based on the trained data.

Why is it important to save the testing model and cross-validation results?

It is important to save the testing model and cross-validation results for future reference, as these saved processes help in tracking the model's performance and ensuring repeatable results when necessary.