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Categories / Computer Science & IT / Artificial Intelligence and Machine Learning / Artificial Intelligence and Machine Learning - Part 7

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Artificial Intelligence and Machine Learning - Part 7

Question 1 of 15 Hints left: 3

Q1

What does 'model evaluation' involve?

A Selecting model parameters
B Feature scaling
C Data imputation
D Assessing the performance of a model

Q2

What does 'bias-variance tradeoff' refer to in machine learning?

A The balance between model complexity and performance
B The balance between data and features
C The balance between training and test data
D The balance between different algorithms

Q3

What is the 'Elbow Method'?

A A way to assess model performance
B A method for feature scaling
C A technique for hyperparameter tuning
D A technique used to determine the optimal number of clusters in k-Means clustering

Q4

Which algorithm is known for its use in supervised learning?

A PCA
B DBSCAN
C K-Means Clustering
D Naive Bayes

Q5

What is 'Transfer Learning'?

A A technique for feature scaling
B A way to handle missing values
C A method for data imputation
D Using a pre-trained model on a new but related task to leverage previously learned features

Q6

What is 'Feature Scaling'?

A Encoding categorical variables
B Creating new features from existing ones
C Transforming features to a common scale to improve model performanc
D Reducing dimensionality

Q7

Which metric is used to measure the quality of a regression model?

A Recall
B Precision
C R-squared
D F1-Score

Q8

What is the difference between 'bagging' and 'boosting'?

A Bagging improves model accuracy; boosting improves model stability
B Bagging and boosting are the same
C Bagging improves model stability; boosting improves model accuracy
D Bagging is used for regression; boosting is used for classification

Q9

What is 'feature extraction' in machine learning?

A Selecting important features
B Combining different models
C Creating new features from existing data
D Scaling features

Q10

What is 'Ensemble Learning'?

A Reducing dimensionality
B Training a single model
C Scaling features
D Combining multiple models to improve overall performance

Q11

What does 'Dimensionality Curse' refer to?

A The challenges and issues that arise when working with high-dimensional data
B The benefits of high-dimensional data
C The ease of visualization with high-dimensional data
D The simplicity of high-dimensional data

Q12

What is 'Feature Selection'?

A Normalizing the features
B Encoding categorical variables
C Choosing the most important features for building a model
D Generating new features

Q13

Which algorithm is commonly used for classification problems?

A K-Nearest Neighbors
B Principal Component Analysis
C Linear Regression
D K-Means Clustering

Q14

What is 'Self-Organizing Map'?

A A type of neural network used for unsupervised learning and dimensionality reduction
B A tool for clustering data
C A method for regression analysis
D A technique for feature extraction

Q15

Which of the following is not a type of neural network architecture?

A Recurrent Neural Network
B Generative Adversarial Network
C Convolutional Neural Network
D Decision Tree

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