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Machine Learning MCQ
18 Multiple Choice Questions with Explanations
Test your knowledge of Machine Learning fundamentals, algorithms, and concepts.
Machine Learning Questions
18 Questions2
Which algorithm is used for classification problems?
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What is overfitting in machine learning?
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Which technique is used to prevent overfitting?
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What does the bias-variance tradeoff represent?
6
What is cross-validation used for?
7
Which is an example of unsupervised learning?
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What is the purpose of feature scaling?
9
What is a decision tree?
10
What is ensemble learning?
11
In the context of Support Vector Machines (SVM), what is the purpose of the kernel trick?
12
Consider a dataset with highly imbalanced classes (e.g., 99% Class A, 1% Class B). Which evaluation metric is the LEAST informative for assessing model performance?
13
In Principal Component Analysis (PCA), what do the eigenvectors of the covariance matrix represent?
14
Why does L1 regularization (Lasso) tend to produce sparse models compared to L2 regularization (Ridge)?
15
In the context of ensemble methods, how does XGBoost fundamentally differ from Random Forest?
16
When diagnosing a severe disease where early detection is critical, which evaluation threshold tuning strategy is most appropriate?
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What is the primary advantage of the K-Means++ initialization algorithm over standard K-Means random initialization?
18
In k-Fold Cross-Validation, what happens to the variance of the performance estimate as the number of folds (k) approaches the number of samples (Leave-One-Out Cross-Validation)?
About Machine Learning MCQ
These Machine Learning multiple choice questions are designed to help you prepare for technical interviews, competitive exams, and certifications. Each question includes a detailed explanation to help you understand the underlying concepts.
Machine Learning is a fundamental topic in Artificial Intelligence and Machine Learning. Practicing these MCQs will help you strengthen your understanding and identify areas where you need more study.