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WorksheetsISP610 REVISION RAPIDMINER
Total questions: 20
Worksheet time: 13mins
Which operator in RapidMiner is primarily used to load data from a file into the process?
Normalize
Read CSV
Join
Apply Model
What does the 'Filter Examples' operator do in RapidMiner?
Normalizes the dataset
Removes all columns from the dataset
Removes rows based on specified conditions
Combines two datasets
Which operator in RapidMiner is used to remove unwanted columns from a dataset?
Select Attributes
Filter Examples
Remove Duplicates
Set Role
Which operator is used to train a decision tree in RapidMiner?
Decision Tree
Apply Model
Generate Attributes
Select Attributes
What is the purpose of the 'Apply Model' operator in RapidMiner?
Removes attributes from a dataset
Tests multiple models at once
Builds a model based on training data
Applies a previously trained model to new data
What is the purpose of the ‘Split Data’ operator in RapidMiner?
It combines multiple datasets
It deletes missing values
It splits attributes into separate files
It divides the data into training and testing sets
In RapidMiner, what does the 'Cross Validation' operator do?
Filters out duplicate entries
Trains and tests a model to evaluate its performance
Visualizes data correlations
Combines two datasets
Which plot best used to describe the distribution of dataset?
Pie Chart
Histogram
Wordcloud
Bar Chart
Which visualization method is best for understanding the relationship between two continuous variables in RapidMiner?
Scatter Plot
Bar Chart
Pie Chart
Box Plot
.Which performance metric is most appropriate for evaluating classification models in RapidMiner?
Mean Absolute Error
Root Mean Squared Error
Accuracy
R-squared
What is the use of the 'Performance (Classification)' operator in RapidMiner?
To calculate performance metrics like precision, recall, and accuracy
To generate new features
To visualize the prediction results
To optimize model parameters
Which of this value is the False Positive if the Churn=Positive
128
57
23
91
You built a churn prediction model in RapidMiner. The model has high accuracy but a low F1 score. What does this likely mean?
The model works well for both classes.
The data is balanced.
The model is too complex.
The model is good at predicting the majority group but not the minority group.
Your churn model has high recall but low precision. What does this tell you, and how can you fix it?
The model finds most churners but also wrongly predicts non-churners as churners; try changing the threshold or model.
The model misses many churners; lower the threshold.
The model needs more attributes.
The model is too complex; simplify it.
This is a result from decision tree model to classify churn customer. What can be concluded from this confusion matrix?
The model is more precise at predicting loyal customers than churners.
The model is better at recalling (identifying) loyal customers than churners.
The model predicts churners more accurately than loyal customers.
The model has perfect accuracy for both churn and loyal customers.
What is the value of False Negative if Churn=Positive
407
96
141
354
This figure shows the process to classify churn customer. How to fix this error?
Remove Decision tree operator
Add Set Role Operator and assign an attribute to be label
Remove Performance operator
Add Store operator to save cleaned dataset
This figure shows the process to classify churn customer. How to fix this error?
Change Decision Tree operator to SVM operator.
Change the Performance (Regression) to Performance (classification)
Add Set Role to assign new attribute as label
Use Cross Validation instead of Split Percentage
The diagram illustrates the modelling process for the Titanic training data using a Decision Tree and the hold-out method. Identify and name each operator in the process.
Cross Validation > Decision Tree > Apply Model >
Performance (Regression)
Split Data > Decision Tree > Apply Model >
Performance (Classification)
Split Data > Decision Tree >
Performance (Classification) > Apply Model
Cross Validation > Decision Tree > Performance (Classification) > Apply Model
The diagram above shows the testing phase of a Cross Validation process using a Random Forest model but the port connections are missing. Which of the following options shows the CORRECT port connections to complete the testing phase?
1. exa (Random Forest) → unl (Apply Model)
2. mod (Apply Model) → lab (Performance)
1. exa (Random Forest) → lab (Apply Model)
2. lab (Apply Model) → per (Performance)
1. thr (Cross Validation) → unl(Apply Model)
2. lab (Apply Model) → lab(Performance)
1. tes (Cross Validation) → unl (Apply Model)
2. lab (Apply Model) → lab (Performance)
