IBM Data Science Practice Test

If studying the problem of employee attrition with a CSV file of various metrics, which model would be the best fit?

1. Binary classification

2. Multiclass classification

3. Convolutional networks

4. Linear regression

The correct answer is: Binary classification

When addressing the problem of employee attrition using a dataset that contains various metrics, binary classification is the most suitable modeling approach. This is because employee attrition typically involves a binary outcome: an employee either leaves the company (attrition occurs) or remains in the company (attrition does not occur). In binary classification, the model is designed to predict one of two categories. In this case, the two categories would be "attrition" and "no attrition." This aligns well with the nature of the problem, as it directly aims to identify whether an individual employee will leave or stay based on the metrics available in the dataset. Other modeling options, such as multiclass classification, are unnecessary here because the problem does not involve multiple categories or classes beyond the simple binary outcome. Convolutional networks are more suited for image processing tasks and are not applicable to tabular data typically represented in a CSV file. Linear regression, while useful for predicting continuous outcomes, is not appropriate for a situation where the objective is to classify categorical outcomes. Thus, binary classification stands out as the optimal choice for analyzing employee attrition.