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Categories / Computer Science & IT / Artificial Intelligence and Machine Learning / Artificial Intelligence and Machine Learning - Part 2

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Artificial Intelligence and Machine Learning - Part 2

Question 1 of 25 Hints left: 3

Q1

What is 'Stochastic Gradient Descent' (SGD)?

A An optimization algorithm that updates parameters using a subset of data
B A clustering algorithm
C A technique for model evaluation
D A method for feature selection

Q2

Which model is known for being a 'black-box' in terms of interpretability?

A Decision Trees
B Neural Networks
C Naive Bayes
D Linear Regression

Q3

What is a common technique for handling missing values in a dataset?

A Removing rows with missing values
B Imputing missing values
C Encoding categorical variables
D Normalizing the data

Q4

What does 'ROC Curve' measure in a classification model?

A The accuracy of the model
B The error rate of the model
C The trade-off between true positive rate and false positive rate
D The precision of the model

Q5

What is the purpose of the 'validation set' in machine learning?

A To scale the features
B To train the model
C To evaluate model performance on unseen data
D To tune hyperparameters

Q6

Which method is used to evaluate model performance for classification tasks?

A Mean Squared Error (MSE)
B Confusion Matrix
C Mean Absolute Error (MAE)
D ROC Curve

Q7

What is 'L1 Regularization'?

A A technique for feature selection
B A form of data augmentation
C A regularization technique that adds the absolute value of the coefficients to the loss function
D A method for scaling features

Q8

What is the main advantage of using a Support Vector Machine (SVM)?

A It works well with high-dimensional data
B It is simpler to implement
C It requires less data
D It is faster than other algorithms

Q9

Which method is used to assess the importance of features in a model?

A Feature Scaling
B Feature Extraction
C Feature Selection
D Feature Engineering

Q10

What does 'AUC' stand for in the context of ROC curves?

A Area Under Curve
B Average Utility Coefficient
C Automated User Classification
D Accuracy Under Curve

Q11

What is 'Bagging' in ensemble learning?

A Training a single model on the entire dataset
B Reducing the number of features in data
C Training multiple models on different subsets of data and averaging their predictions
D Scaling features for better performance

Q12

Which evaluation metric is used for regression problems?

A Precision
B Recall
C F1 Score
D Mean Squared Error

Q13

What is 'support vector machine' (SVM) used for in machine learning?

A Clustering
B Dimensionality reduction
C Classification and regression
D Feature extraction

Q14

What is 'Cross-Validation'?

A A technique for feature scaling
B A technique to assess the performance of a model by dividing data into multiple subsets and evaluating on each
C A method for dimensionality reduction
D A method to handle missing values

Q15

What is 'K-Nearest Neighbors' (KNN)?

A A clustering algorithm
B A regression algorithm
C A dimensionality reduction technique
D A classification algorithm based on the distance between data points

Q16

What is the purpose of feature scaling in machine learning?

A To bring features to a similar scale
B To improve model accuracy
C To reduce the number of features
D To select important features

Q17

What does 'regularization' do in machine learning?

A Speeds up training
B Increases model complexity
C Prevents overfitting
D Normalizes the data

Q18

What is 'overfitting' in machine learning?

A When the model is too simple
B When the model generalizes well to new data
C When the model performs well on test data
D When the model performs well on training data but poorly on new data

Q19

What does 'AutoML' refer to?

A Automated Model Selection
B Automated Machine Learning
C Automated Data Scaling
D Automated Data Imputation

Q20

What does 'Data Imputation' refer to?

A Scaling data to a common range
B Normalizing features
C Filling in missing values in a dataset
D Selecting important features

Q21

What is 'data wrangling' in the context of data science?

A Collecting data from various sources
B Analyzing data for patterns
C Visualizing data trends
D Transforming data into a usable format

Q22

What is 'Early Stopping'?

A A method for feature scaling
B A technique for data augmentation
C A technique to stop training a model once performance stops improving on a validation set
D A way to encode categorical variables

Q23

Which algorithm is particularly well-suited for time series prediction?

A Naive Bayes
B Long Short-Term Memory (LSTM)
C Decision Trees
D Random Forest

Q24

What is 'Autoencoder'?

A A technique for clustering data
B A tool for feature selection
C A method for supervised classification
D A type of neural network used for unsupervised learning of efficient codings

Q25

What does 'One-Hot Encoding' do?

A Encodes numerical features into categories
B Normalizes numerical features
C Reduces dimensionality
D Converts categorical variables into binary vectors

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