How would you handle missing data in a dataset?
Theme: Data Preprocessing Role: Machine Learning Engineer Function: Technology
Interview Question for Machine Learning Engineer: See sample answers, motivations & red flags for this common interview question. About Machine Learning Engineer: Builds machine learning models and algorithms. This role falls within the Technology function of a firm. See other interview questions & further information for this role here
Sample Answer
Example response for question delving into Data Preprocessing with the key points that need to be covered in an effective response. Customize this to your own experience with concrete examples and evidence
- Identifying missing data: First, I would start by identifying the missing data in the dataset. This can be done by checking for null values or using statistical methods like summary statistics or visualization techniques
- Understanding the reasons for missing data: Next, it is important to understand the reasons for missing data. It could be due to various factors such as data entry errors, equipment malfunction, or intentional non-response. Understanding the reasons helps in determining the appropriate handling technique
- Handling missing data: There are several techniques to handle missing data. Some common approaches include: 1. Deleting rows or columns with missing data if the missingness is minimal and doesn't significantly impact the analysis. 2. Imputing missing values using statistical methods like mean, median, or mode. This is suitable when the missingness is random and doesn't introduce bias. 3. Using advanced imputation techniques like regression imputation or multiple imputation when the missingness is non-random and there is a pattern to the missing data. 4. Creating a separate category or indicator variable to represent missing values if they hold some meaningful information. The choice of technique depends on the nature and extent of missing data, as well as the specific requirements of the analysis
- Evaluating the impact of missing data handling: After handling the missing data, it is crucial to evaluate the impact of the chosen technique. This can be done by comparing the results before and after handling missing data, assessing the changes in statistical measures, or conducting sensitivity analyses. It is important to ensure that the chosen technique doesn't introduce bias or distort the analysis
- Documenting the missing data handling process: Lastly, it is essential to document the missing data handling process. This includes recording the techniques used, the rationale behind the choices, and any assumptions made during the process. Proper documentation ensures transparency and reproducibility of the analysis
Underlying Motivations
What the Interviewer is trying to find out about you and your experiences through this question
- Problem-solving skills: Ability to handle missing data effectively and efficiently
- Technical knowledge: Understanding of various techniques and algorithms for handling missing data
- Experience: Previous experience in dealing with missing data and its impact on machine learning models
- Critical thinking: Ability to assess the impact of missing data on the overall analysis and make informed decisions
Potential Minefields
How to avoid some common minefields when answering this question in order to not raise any red flags
- Lack of knowledge: Not being aware of common techniques for handling missing data, such as imputation or deletion
- Overconfidence: Claiming that missing data is not a problem or can be ignored without providing a valid justification
- Inflexibility: Not considering different approaches for handling missing data and sticking to only one method
- Ignoring potential biases: Failing to address the potential biases introduced by handling missing data in a specific way
- Lack of communication skills: Not being able to explain the chosen method for handling missing data clearly and concisely