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Machine Learning Algorithms

Which kernel is default in sklearn SVC?

1. A-Linear
2. B-Poly
3. C-Sigmoid
4. D-RBF
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Which ensemble averages probabilities, not votes?

1. A-Hard voting
2. B-Soft voting
3. C-Stacking
4. D-Bagging
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Which algorithm needs no learning rate?

1. A-Gradient Boosting
2. B-SVM
3. C-Decision Tree
4. D-Neural Net
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In machine learning, what is the primary purpose of the reinforcement learning technique?

1. A-Learn through interaction
2. B-Supervised learning
3. C-Unsupervised learning
4. D-Semi-supervised learning
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Which theorem establishes that any smooth function can be approximated by neural networks?

1. A-Universal Approximation Theorem
2. B-No-Free-Lunch Theorem
3. C-Bayes' Theorem
4. D-Central Limit Theorem
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In machine learning, what is the primary purpose of the batch normalization technique?

1. A-Reduce internal covariate shift
2. B-Accelerate training
3. C-Prevent overfitting
4. D-Improve generalization
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In machine learning, what is the primary purpose of the dropout technique?

1. A-Prevent overfitting
2. B-Accelerate training
3. C-Reduce memory usage
4. D-Improve convergence
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In machine learning, what is the primary purpose of the Adam optimization algorithm?

1. A-Adaptive learning rates
2. B-Gradient clipping
3. C-Regularization
4. D-Batch normalization
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In machine learning, what is the primary challenge addressed by batch normalization?

1. A-Vanishing gradients
2. B-Internal covariate shift
3. C-Overfitting
4. D-Underfitting
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In machine learning, what is the primary purpose of the attention mechanism in transformer models?

1. A-Weight initialization
2. B-Dynamic focus on input parts
3. C-Gradient computation
4. D-Regularization
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In machine learning, what is the primary challenge addressed by the vanishing gradient problem in deep neural networks?

1. A-Overfitting in convolutional networks
2. B-Exponential decrease in gradient magnitude
3. C-Catastrophic forgetting in reinforcement learning
4. D-Local minima convergence in optimization
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What ethical considerations are relevant in the context of data science and machine learning?

1. A-Privacy and data ownership
2. B-Speed and efficiency only
3. C-Algorithm complexity
4. D-Hardware requirements
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In time series analysis, what does "seasonality" refer to?

1. A-Analyzing data over a specific period
2. B-Identifying patterns that repeat at regular intervals
3. C-Predicting future outcomes based on historical data
4. D-Decomposing time series into components
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What does A/B testing aim to achieve in the context of machine learning for Google rankings?

1. A-Identify website design preferences
2. B-Compare two versions to determine performance
3. C-Analyze user behavior on social media
4. D-Implement reinforcement learning algorithms
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Which type of machine learning model is inspired by the structure and function of the human brain?

1. A-Decision trees
2. B-Support vector machines
3. C-Neural networks
4. D-K-means clustering
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What is the primary goal of predictive analytics in machine learning?

1. A-Analyzing historical data
2. B-Identifying patterns in real-time data
3. C-Making predictions about future outcomes
4. D-Creating interactive visualizations
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Which technology is often associated with processing and analyzing large datasets in machine learning?

1. A-SQL
2. B-JavaScript
3. C-Apache Hadoop
4. D-HTML
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In the context of machine learning, what does "ensemble methods" refer to?

1. A-Algorithms that work in isolation
2. B-Combining predictions from multiple models
3. C-Methods for feature engineering
4. D-Sequential learning techniques
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What does NLP stand for in the context of machine learning?

1. A-Natural Language Processing
2. B-Numeric Logic Programming
3. C-Neural Learning Protocol
4. D-Nonlinear Pattern Recognition
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Which type of machine learning algorithm is used for predicting a continuous outcome, such as house prices?

1. A-Classification
2. B-Clustering
3. C-Regression
4. D-Reinforcement learning
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What is the primary objective of machine learning algorithms?

1. A-What is the primary objective of machine learning algorithms?
2. B-Automate manual data entry
3. C-Learn patterns from data and make predictions
4. D-Generate random outputs
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