Data Science Interview Questions – Key ML Terminologies
Q1. (Asked in Infosys System Engineer Interview)
What is a Dataset in Machine Learning?
Answer:
A dataset is a collection of data used to train and test machine learning models. It includes features (inputs) and sometimes labels (outputs) depending on the problem type.
Q2. (Asked in TCS Ninja Interview)
What are Features in ML?
Answer:
Features are the input variables (also called predictors or attributes) used by the model to make predictions.
Example: For a housing price model, features can be area, location, and number of bedrooms.
Q3. (Asked in Capgemini Data Analyst Interview)
What is a Label in ML?
Answer:
A label is the output or result that the model is trying to predict.
Example: In spam detection, the label is “spam” or “not spam”.
Q4. (Asked in Wipro Data Science Interview)
What is a Model in ML?
Answer:
A model is the mathematical representation learned from data. It maps inputs (features) to outputs (labels) and is used to make predictions.
Q5. (Asked in Cognizant GenC Interview)
What do Training and Testing data mean?
Answer:
- Training Data is used to teach the model.
- Testing Data is used to check how well the model performs on new, unseen data.
Q6. (Asked in IBM Data Scientist Role)
What is Overfitting in ML?
Answer:
Overfitting happens when a model performs well on training data but poorly on new data. It memorizes noise instead of learning patterns.
Q7. (Asked in HCL Analyst Interview)
What is Underfitting in ML?
Answer:
Underfitting means the model is too simple and fails to learn patterns even from the training data, leading to poor performance.
Q8. (Asked in Deloitte USI Interview)
What is Hyperparameter in ML?
Answer:
Hyperparameters are settings that control the model’s learning process, like learning rate, number of trees, or depth of a decision tree. They are not learned from data but set manually or through tuning.
Q9. (Asked in Tech Mahindra Interview)
What is the difference between Classification and Regression?
Answer:
- Classification is used for predicting categories (e.g., Yes/No, Spam/Not Spam).
- Regression is used for predicting continuous values (e.g., price, salary).
Q10. (Asked in Amazon Applied Scientist Interview)
What is a Confusion Matrix?
Answer:
A confusion matrix is a table used to evaluate classification models. It shows how many predictions were correct or incorrect across actual vs. predicted classes.
