What is the purpose of feature scaling in machine learning?
Theme: Machine Learning Concepts 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 Machine Learning Concepts with the key points that need to be covered in an effective response. Customize this to your own experience with concrete examples and evidence
- Importance of Feature Scaling: Feature scaling is an important preprocessing step in machine learning to ensure that all features have the same scale
- Normalization: Feature scaling is also known as normalization, where the values of features are transformed to a specific range
- Avoiding Bias: Feature scaling helps to avoid bias towards features with larger scales, as it can dominate the learning algorithm
- Improving Convergence: Feature scaling can improve the convergence speed and performance of many machine learning algorithms
- Distance-Based Algorithms: Feature scaling is particularly crucial for distance-based algorithms, such as k-nearest neighbors and support vector machines, as they are sensitive to the scale of features
- Gradient Descent Optimization: Feature scaling can help gradient descent optimization algorithms converge faster by preventing oscillations and overshooting
- Regularization: Feature scaling is often required when using regularization techniques, such as L1 or L2 regularization, to ensure fair regularization across features
- Standardization vs. Normalization: There are two common methods of feature scaling: standardization (subtracting mean and dividing by standard deviation) and normalization (scaling to a specific range, e.g., [0, 1]). The choice depends on the requirements of the specific machine learning algorithm
- Exceptions: There are some machine learning algorithms, like tree-based models (e.g., random forests, gradient boosting), that are not affected by feature scaling and can handle features with different scales effectively
Underlying Motivations
What the Interviewer is trying to find out about you and your experiences through this question
- Technical Knowledge: Assessing the candidate's understanding of feature scaling and its purpose in machine learning
- Problem-solving Skills: Evaluating the candidate's ability to identify and address issues related to feature scaling in machine learning models
- Awareness of Best Practices: Determining if the candidate is familiar with common techniques used in machine learning, such as feature scaling, to improve model performance
Potential Minefields
How to avoid some common minefields when answering this question in order to not raise any red flags
- Lack of understanding: Not being able to explain the purpose of feature scaling accurately or providing incorrect information
- Vague response: Providing a general or unclear explanation without mentioning specific benefits or use cases of feature scaling
- Limited knowledge: Not being aware of different feature scaling techniques or their impact on different machine learning algorithms
- Overconfidence: Claiming that feature scaling is not necessary or dismissing its importance without providing valid reasons
- Inability to apply knowledge: Not being able to explain how feature scaling can improve model performance or address issues like different scales or units of features