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Categories / Computer Science & IT / Artificial Intelligence and Machine Learning / Artificial Intelligence and Machine Learning - Part 6

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Artificial Intelligence and Machine Learning - Part 6

Question 1 of 25 Hints left: 3

Q1

What is 'Time Series Forecasting'?

A Reducing dimensionality
B Predicting future values based on past data
C Classifying data into categories
D Clustering data into groups

Q2

What is 'Deep Neural Network'?

A A neural network with multiple hidden layers
B A method for dimensionality reduction
C A neural network with a single hidden layer
D A network for clustering

Q3

What does 'ensemble learning' involve?

A Selecting the best features
B Training a single model
C Combining multiple models
D Splitting data into different subsets

Q4

What does 'AUC' represent in ROC analysis?

A Area Under Classification
B Area Under Cost
C Area Under Computation
D Area Under the Curve

Q5

What is 'Outlier Detection'?

A A technique for feature scaling
B A tool for encoding categorical variables
C A method for dimensionality reduction
D Identifying and handling data points that differ significantly from other data

Q6

What is 'cross-validation' used for in machine learning?

A To evaluate model performance
B To split data into training and test sets
C To tune hyperparameters
D To preprocess data

Q7

Which method is commonly used for dimensionality reduction in text data?

A t-Distributed Stochastic Neighbor Embedding (t-SNE)
B Principal Component Analysis (PCA)
C k-Means Clustering
D Latent Semantic Analysis (LSA)

Q8

What is 'Precision-Recall Curve'?

A A way to handle missing values
B A plot that shows the trade-off between precision and recall for different thresholds
C A technique for clustering
D A method for scaling features

Q9

What is 'overfitting' in the context of machine learning models?

A The model performs well on unseen data
B The model performs well on training data
C The model performs poorly on training data
D The model performs well on all data

Q10

Which algorithm is often used for time series forecasting?

A Naive Bayes
B K-Means Clustering
C ARIMA
D Linear Regression

Q11

What is 'natural language processing' (NLP) used for?

A Time series forecasting
B Predicting numerical values
C Analyzing and processing human language
D Image recognition

Q12

What does 'bootstrapping' involve in statistical methods?

A Resampling with replacement
B Data normalization
C Model selection
D Feature extraction

Q13

What is 'Regularization' in machine learning?

A A technique to prevent overfitting by adding a penalty to the model's complexity
B A form of data augmentation
C A technique for feature selection
D A method for scaling features

Q14

What is 'dimensionality reduction' used for in data science?

A To increase the number of features
B To split data into subsets
C To reduce the number of features while retaining important information
D To normalize feature scales

Q15

What is 'Markov Chain'?

A A stochastic process that models a system that transitions from one state to another based on probabilities
B A type of feature extraction
C A method for clustering
D A technique for dimensionality reduction

Q16

What is the primary purpose of a Confusion Matrix in Machine Learning?

A To predict data
B To scale data
C To evaluate performance
D To measure accuracy

Q17

Which algorithm is used for clustering data into distinct groups?

A k-Means Clustering
B Decision Trees
C Support Vector Machine (SVM)
D Principal Component Analysis (PCA)

Q18

What is 'Reinforcement Learning'?

A A technique for data normalization
B A method for supervised learning
C A tool for feature extraction
D A type of machine learning where an agent learns to make decisions by receiving rewards or penalties

Q19

What is 'LightGBM'?

A A type of neural network
B A clustering algorithm
C A gradient boosting framework that is efficient and scalable
D A method for feature selection

Q20

Which of the following methods is used for dimensionality reduction?

A Decision Trees
B Random Forest
C Support Vector Machines
D Principal Component Analysis

Q21

What does 'loss function' measure in a machine learning model?

A The model's complexity
B The difference between predicted and actual values
C The accuracy of the model
D The time taken to train the model

Q22

What is 'Bias in Machine Learning'?

A Error due to underfitting
B Error due to overfitting
C Systematic error introduced by the model's assumptions
D Random variability in data

Q23

What is 'feature engineering' in machine learning?

A Creating new features from existing data
B Selecting the most important features
C Reducing the number of features
D Scaling features to a common range

Q24

What is 'Logistic Regression' used for?

A Dimensionality reduction
B Binary classification problems
C Regression analysis
D Clustering data

Q25

What is the purpose of 'grid search' in machine learning?

A To optimize hyperparameters
B To evaluate model performance
C To scale features
D To split data into training and testing sets

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