What is the difference between supervised and unsupervised 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
- Definition: Supervised learning is a type of machine learning where the model is trained on labeled data, meaning the input data is accompanied by the correct output. Unsupervised learning, on the other hand, is a type of machine learning where the model is trained on unlabeled data, meaning the input data does not have any corresponding output
- Objective: Supervised learning aims to learn a mapping function from input variables to output variables, allowing the model to make predictions on unseen data. Unsupervised learning, on the other hand, aims to discover hidden patterns or structures in the input data without any specific output goal
- Training Process: In supervised learning, the model is trained using labeled data, where the input and output pairs are provided. The model learns from these examples and tries to generalize the mapping function to make predictions on new, unseen data. In unsupervised learning, the model is trained using unlabeled data, and it tries to find patterns or clusters in the data without any specific guidance
- Types of Output: Supervised learning produces a model that can predict the correct output for new, unseen inputs. Unsupervised learning, on the other hand, produces a model that can uncover hidden patterns or structures in the data, such as clusters or associations
- Examples: Examples of supervised learning algorithms include linear regression, logistic regression, and support vector machines. Examples of unsupervised learning algorithms include clustering algorithms like k-means and hierarchical clustering, as well as dimensionality reduction techniques like principal component analysis (PCA)
Underlying Motivations
What the Interviewer is trying to find out about you and your experiences through this question
- Knowledge: Assessing the candidate's understanding of fundamental concepts in machine learning
- Experience: Determining if the candidate has practical experience in applying supervised and unsupervised learning algorithms
- Problem-solving: Evaluating the candidate's ability to identify appropriate learning methods based on the problem at hand
- Communication: Assessing the candidate's ability to explain complex concepts in a clear and concise manner
Potential Minefields
How to avoid some common minefields when answering this question in order to not raise any red flags
- Confusing or incorrect definitions: Providing inaccurate or unclear definitions of supervised and unsupervised learning
- Lack of understanding of use cases: Not being able to explain the typical use cases or applications of supervised and unsupervised learning
- Inability to differentiate between the two: Failing to highlight the key differences between supervised and unsupervised learning, such as the presence or absence of labeled data
- Limited knowledge of algorithms: Not being familiar with common algorithms used in supervised and unsupervised learning, such as decision trees or k-means clustering
- Failure to mention evaluation metrics: Neglecting to discuss the evaluation metrics used to measure the performance of supervised and unsupervised learning models
- Lack of awareness of challenges: Not acknowledging the challenges or limitations associated with supervised and unsupervised learning, such as overfitting or the curse of dimensionality