✅ Top AI Interview Questions with Answers: Part-4 🧠

31. Explain Decision Trees and Random Forest
• Decision Tree is a flowchart-like structure where internal nodes represent tests on features, branches represent outcomes, and leaf nodes represent final decisions.
• Random Forest is an ensemble of decision trees trained on different subsets of data and features. It improves accuracy and reduces overfitting by averaging multiple trees' results.

32. What is a Support Vector Machine (SVM)?
SVM is a supervised learning model that finds the best hyperplane to separate classes with the maximum margin. It works well for both linear and non-linear data using kernel functions (e.g., RBF, polynomial).

33. What is ensemble learning?
It combines predictions from multiple models to improve performance.
• Increases robustness
• Reduces overfitting
• Common types: Bagging, Boosting, Stacking

34. What is bagging vs boosting?
• Bagging: Trains models independently on random data samples (e.g., Random Forest). Reduces variance.
• Boosting: Trains models sequentially, where each corrects the previous (e.g., XGBoost). Reduces bias.

35. What is cross-validation?
A technique to evaluate model performance by dividing the dataset into k folds. The model is trained on (k−1) folds and tested on the remaining fold. Repeats k times to ensure reliability.

36. Explain ROC curve and AUC
• ROC (Receiver Operating Characteristic) curve plots True Positive Rate vs False Positive Rate.
• AUC (Area Under Curve) measures the area under the ROC. Closer to 1 means better performance.

37. What is an autoencoder?
An unsupervised neural network used for dimensionality reduction.
• It learns to encode input into a lower-dimensional space and decode it back to the original.
• Used for denoising, anomaly detection, etc.

38. What are GANs (Generative Adversarial Networks)?
GANs consist of two networks:
• Generator: Creates fake data
• Discriminator: Tries to distinguish real from fake
They train adversarially to generate realistic outputs (e.g., deepfake images, art).

39. Explain LSTM and GRU
• LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) are RNN variants that solve the vanishing gradient problem.
• Used in sequence modeling (e.g., text, time series).
• GRU is faster with fewer parameters; LSTM is more expressive.

40. What is NLP and its applications?
Natural Language Processing is a branch of AI that deals with human language.
Applications:
• Chatbots
• Sentiment analysis
• Language translation
• Text summarization
• Speech recognition

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