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Categories / Computer Science & IT / Artificial Intelligence and Machine Learning / Artificial Intelligence and Machine Learning - Part 3

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Artificial Intelligence and Machine Learning - Part 3

Question 1 of 25 Hints left: 3

Q1

What does 'Shallow Learning' refer to?

A Reducing the dimensionality of data
B Performing clustering analysis
C Using machine learning models with a small number of layers
D Using deep neural networks

Q2

What is 'Overfitting'?

A When a model performs well on training data but poorly on new data
B When a model performs poorly on both training and test data
C When a model has too few features
D When a model generalizes well to new data

Q3

What is the key difference between 'supervised learning' and 'unsupervised learning'?

A Supervised learning is more complex than unsupervised learning
B Supervised learning uses unlabeled data; unsupervised learning uses labeled data
C Supervised learning requires more data; unsupervised learning requires less data
D Supervised learning uses labeled data; unsupervised learning uses unlabeled data

Q4

What is the main advantage of using Random Forests in machine learning?

A Faster than Decision Trees
B Less prone to overfitting
C Easy to interpret
D Requires less data

Q5

What does 'data augmentation' involve in machine learning?

A Selecting important features
B Creating new data from existing data
C Normalizing data
D Reducing the number of features

Q6

What is 'Backpropagation' used for in neural networks?

A Training the network by adjusting weights based on the error gradient
B Reducing dimensionality
C Encoding categorical variables
D Normalizing data

Q7

What is 'Hyperparameter'?

A A feature used in the model
B An evaluation metric
C A parameter set before the learning process begins, which controls the learning process
D A parameter learned during the training process

Q8

What is 'Neural Network'?

A A method for feature scaling
B A computational model inspired by the human brain, consisting of layers of interconnected nodes
C A type of decision tree
D A clustering algorithm

Q9

What is the purpose of 'hyperparameter tuning'?

A To preprocess the data
B To adjust model parameters for optimal performance
C To select the best model
D To train the model

Q10

What is the main use of 'confusion matrix' in classification?

A To preprocess data
B To evaluate classification model performance
C To scale features
D To split data into subsets

Q11

What is 'Underfitting'?

A When a model performs poorly on both training and new data due to being too simple
B When a model generalizes well to new data
C When a model performs well on training data but poorly on new data
D When a model is too complex

Q12

What is a 'hyperparameter' in machine learning?

A A parameter learned from the data
B A parameter set before training
C A parameter used for feature selection
D A parameter used to scale data

Q13

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

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

Q14

What does 'Learning Rate' refer to in Gradient Descent?

A The step size at each iteration while moving toward a minimum of the loss function
B The number of epochs used in training
C The size of the training dataset
D The complexity of the model

Q15

What is 'Support Vector Regression' (SVR)?

A A regression algorithm that finds the hyperplane that best fits the data with a margin of tolerance
B A type of neural network
C A method for clustering
D A technique for feature scaling

Q16

What is 'Random Forest'?

A An ensemble learning method that combines multiple decision trees
B A method for data augmentation
C A technique for feature scaling
D A single decision tree model

Q17

In which scenario is 'k-fold cross-validation' commonly used?

A Data preprocessing
B Hyperparameter tuning
C Model evaluation
D Feature selection

Q18

What is 'Deep Learning'?

A A method for clustering data
B A technique for dimensionality reduction
C A type of decision tree algorithm
D A subset of machine learning using neural networks with many layers

Q19

What is 'Gradient Boosting' in ensemble learning?

A A method for combining multiple models in parallel
B A technique that builds models sequentially to correct errors of previous models
C A technique for dimensionality reduction
D A method for clustering data

Q20

What is 'Label Encoding'?

A Converting categorical values into numerical values
B Encoding numerical features into categorical values
C Performing feature scaling
D Normalizing numerical values

Q21

Which algorithm is used for dimensionality reduction and feature extraction?

A Random Forest
B Linear Regression
C Support Vector Machine (SVM)
D Principal Component Analysis (PCA)

Q22

What is 'Gradient Descent'?

A A technique for dimensionality reduction
B A method for data normalization
C An optimization algorithm that adjusts model parameters to minimize the loss function
D A technique for feature scaling

Q23

What is 'Normalization' in the context of data preprocessing?

A Handling missing values
B Adjusting data values to a standard range
C Performing feature selection
D Encoding categorical variables

Q24

What does 'regularization' do in machine learning models?

A Normalizes feature scales
B Adds a penalty for large coefficients
C Splits data into training and testing sets
D Adds noise to the data

Q25

What is the purpose of 'feature selection' in machine learning?

A To increase the complexity of the model
B To reduce the number of features
C To improve model accuracy
D To generate new features

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