The 12 Best Machine Learning Interview Questions in 2023
What are the best machine learning interview questions? Here are 12 questions that will help you identify the best machine learning developers. Read on!
Interviewing machine learning engineers
Machine learning is a branch of artificial intelligence (AI) and computer science. It focuses on the use of data and algorithms to imitate how humans learn so that it improves its accuracy step by step.
What interview questions should you ask machine learning engineers? Here you go:
Beginner machine learning interview questions
What are the top junior machine learning engineer interview questions? These are the best machine learning questions to get you started:
1. Explain the different types of machine learning.
There are three types of machine learning. These are supervised learning, unsupervised learning, and reinforcement learning.
Supervised machine learning means that a model makes predictions and decisions based on past or labeled data (sets of data that are given tags).
Unsupervised learning means that there is no labeled data, but the model identifies patterns, anomalies, and relationships in the data.
Reinforcement learning is a model where the model learns based on rewards it received for its previous actions.
2. Explain the trade-off between bias and variance.
Bias means that there’s an error because of overly simplistic or erroneous assumptions in the algorithm. The model might underfit your data and make it hard for it to have high predictive accuracy. As a result, you might generalize your knowledge from the training set to the test set.
Variance is an error that’s caused by too much complexity in the algorithm. The model overfits the data because the algorithm is sensitive to a lot of variation in your data.
There shouldn’t be high bias or high variance in your model. Instead, bias and variance have to be traded off because if you add more complexity, you’ll gain variance.
3. Explain overfitting. How can it be avoided?
Overfitting means that a model learns the training set too well, so that it takes up random fluctuations in the training data as concepts. The model’s ability to generalize is impacted.
When a model is then given the training date, it shows full accuracy. But the test data may show an error and low efficiency.
There are many ways to avoid overfitting. You can regularize, which includes a cost term for the features involved with the objective function. Or you can make a simple model as the variance is reduced with lesser variables and parameters. Alternatively, you can use cross-validation methods like k-folds or techniques for regularization like LASSO to penalize these parameters.
4. What are precision and recall?
Recall is a true positive rate or the number of positives that the model claims versus the actual number of positives. Precision (positive predictive value) measures the number of accurate positives when compared to the positives the model claims it contains.
5. What are Python libraries that are used for Data Analysis and Scientific Computations?
The libraries mainly used for Data Analysis are:
6. What is clustering?
Clustering means that a set of objects are grouped into a number of groups. Objects in one group are similar to each other, but dissimilar to objects in other clusters. A few types of clustering are:
Advanced machine learning interview questions
What are the best senior machine learning engineer interview questions? In this section, you learn all about the top interview questions for senior engineers:
7. Define random forest.
A random forest is a supervised machine learning algorithm. It’s generally used for classification problems and it operates by constructing many different trees in the training phase. The random forest uses the decision of the majority of the trees as its final decision.
8. Explain how you handle missing or corrupted data in a dataset.
The easiest way to handle missing or corrupted data is to drop the rows/columns and replace them with another value. You can use two methods in Python Pandas:
- IsNull() and dropna() help you find the columns or rows with missing data and drop them
- Fillna() replaces the wrong values with a placeholder value
9. Define naive in the Naive Bayes Classifier.
A naive classifier makes assumptions that may or may not be true. The algorithm assumes that one feature of a class isn’t related to the presence of other features because of the class variable.
10. Explain how NumPy and SciPy are related.
NumPy is a part of SciPy. It defines arrays together with basic numerical functions (indexing, reshaping, and so on). SciPy means that computations are implemented as numerical integration, optimization and machine learning with NumPy’s functionality.
11. Define the different kernels in SVM.
There are six types of kernels in SVM. These are:
Linear kernel (which is used when data is linearly separable)
Polynomial kernel (which is used with discrete data with no natural notion of smoothness)
Sigmoid kernel (which is used as activation for neural networks)
Radial basis kernel (which is used to create decision boundary to separate two classes)
12. When do you choose classification and when do you choose regression?
Classification means that the target is categorical, while regression means that the target is continuous. They are both supervised machine learning algorithms.
Classification problems include:
Predicting yes or no
Type of color
Regression problems include:
Estimating sales price
Predicting the number of sunny days
Over to you!
There you have it! Now you know what the top machine learning engineer interview questions are.
These interview questions will help you identify top machine learning candidates. But you also need to assess their technical skills
That’s what CodeSubmit helps you with. We offer different types of live coding and assessment tests, including pair programming and take-home coding assessments.