✅ Data Science Interview Questions with Answers Part-6

51. What is machine learning?
Machine learning is a subset of artificial intelligence that enables systems to learn patterns from data and make predictions or decisions without being explicitly programmed. Models improve performance as they see more data.

52. Difference between regression and classification?
Regression predicts continuous numerical values such as price or demand. Classification predicts discrete categories such as yes or no, fraud or not fraud. The choice depends on the nature of the target variable.

53. What is overfitting and underfitting?
Overfitting occurs when a model learns noise and performs well on training data but poorly on new data. Underfitting occurs when a model is too simple to capture patterns. The goal is to balance both for good generalization.

54. What is train-test split?
Train-test split divides data into training and testing sets. The model learns from the training data and is evaluated on unseen test data to measure real-world performance.

55. What is cross-validation?
Cross-validation splits data into multiple folds and trains the model several times using different subsets. It provides a more reliable estimate of model performance and reduces dependency on a single split.

56. What is bias-variance tradeoff?
Bias is error from overly simple models, while variance is error from overly complex models. The tradeoff is about finding a balance where the model generalizes well to unseen data.

57. What is feature selection?
Feature selection is the process of choosing the most relevant variables for modeling. It improves performance, reduces overfitting, and simplifies interpretation by removing redundant or irrelevant features.

58. What is model evaluation?
Model evaluation measures how well a model performs using appropriate metrics. It ensures the model meets both technical accuracy and business requirements before deployment.

59. What is baseline model?
A baseline model is a simple reference model used to set a minimum performance standard. It helps evaluate whether more complex models provide meaningful improvement.

60. How do you choose a model?
Model choice depends on problem type, data size, interpretability needs, performance requirements, and constraints such as latency or resources. Simpler models are preferred unless complexity adds clear value.

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