How would you deal with data that is imbalanced or has missing values?

Machine Learning Engineer Interview Questions

Sample answer to the question

If I come across imbalanced data, I would try using techniques like oversampling the minority class or undersampling the majority class. There're tools in Python to help with that, like imbalanced-learn library. For missing data, I'd assess if it's random or not. If it's missing at random, I might just fill in the gaps with mean or median values, or maybe drop those rows if there's not too much missing. Otherwise, I might need to look into why the data's missing and decide what to do from there.

A more solid answer

In my previous project using a customer dataset, we had an imbalance in the classes we were predicting. First, I assessed the imbalance's impact on the model's accuracy. Then, I applied SMOTE from the imbalanced-learn library to generate synthetic samples for the minority class. This balanced the dataset without losing valuable information. For missing values, I used Python's pandas library to analyze the pattern of missingness. If data was missing at random, I used SimpleImputer to replace missing values with the median, as this is less impacted by outliers. Every step was documented and reviewed with the team.

Why this is a more solid answer:

This solid answer elaborates on a specific example and mentions the use of particular tools, such as Python's pandas and the imbalanced-learn library, demonstrating practical experience and initiative. It indicates some understanding of data analysis and problem-solving. However, it could further tie in communication and teamwork skills, as well as show a dedication to ongoing learning and optimization in line with the job description.

An exceptional answer

In my last role, I encountered imbalanced data while working on a predictive maintenance model for industrial equipment. To address this, I initially evaluated the degree of imbalance and the performance metrics to ensure we focused on precision recall as the dataset was highly imbalanced. Afterward, I employed a combination of SMOTE and Edited Nearest Neighbors to rebalance it. This dual approach helped in synthesizing valuable instances while removing noisy data. For missing values, I delved into exploratory data analysis using R's ggplot2 for a detailed understanding, leading me to discover a pattern in the missingness that related to sensor malfunction. We implemented a feature that marked the likely malfunction, which actually improved our model by acknowledging the missing data as an informative feature. This collaborative effort was appreciated by the team and hailed as a benchmark for our data preprocessing strategy.

Why this is an exceptional answer:

The exceptional answer shows a strong grasp of statistical analysis and problem-solving by discussing the evaluation of performance metrics and the use of a nuanced technique to address imbalance. It also demonstrates the ability to derive insights from missing data, enhancing the model's performance. This level of detail, combined with the emphasis on teamwork and aligning the approach with business needs, aligns well with the job description of a Junior Machine Learning Engineer who must communicate effectively, work well in a team and pursue continued innovation.

How to prepare for this question

Familiarize yourself with various techniques for handling imbalanced data, like random oversampling, SMOTE, and undersampling methods. Understand when to use each one.
Learn to identify the patterns of missing data—missing at random, missing completely at random, or missing not at random. Have strategies prepared for each scenario.
Practice using Python libraries such as imbalanced-learn, pandas, and scikit-learn for data preprocessing tasks. Being able to discuss real scenarios where you've applied these tools will be advantageous.
Be prepared to discuss how your data preprocessing improves model performance and how you ensure your strategies align with business goals. Relate this back to past experiences or projects.
Knowing how to communicate your process and decisions to the team is crucial. Think of examples where your communication skills made a difference in the project's outcome.

What interviewers are evaluating

Data preprocessing
Statistical analysis
Problem-solving