SDS Data science techniques and modelling

Written by Saad Rahman (ACCA)

ACCA · New at Strategic Professional level · 7 questions

Name: ____________________   Date: ____________

1. A model that fits the training data very closely but performs poorly on new data is:

   • A Unbiased
   • B Well calibrated
   • C Underfitted
   • D Overfitted

2. A fraud model flags 50 of 1,000 transactions. 30 of the flagged are real fraud, and there are 40 frauds in total. The precision is:

   • A 97%
   • B 75%
   • C 30%
   • D 60%

3. A fraud model flags 50 of 1,000 transactions. 30 of the flagged are real fraud, and there are 40 frauds in total. The recall is:

   • A 75%
   • B 60%
   • C 97%
   • D 50%

4. Accuracy can be a poor measure of performance when:

   • A The model is simple
   • B There are many columns
   • C Data is numeric
   • D One class is very rare

5. Grouping customers into segments without predefined labels is an example of:

   • A Unsupervised learning (clustering)
   • B Regression
   • C Supervised classification
   • D Hypothesis testing

6. Predicting a customer's monthly spend as a number is a:

   • A Anomaly detection problem
   • B Classification problem
   • C Regression problem
   • D Clustering problem

7. Splitting data into training and test sets is done to:

   • A Reduce the file size
   • B Avoid cleaning
   • C Hide the data
   • D Estimate how the model will perform on unseen data