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Categories / Computer Science & IT / Artificial Intelligence and Machine Learning / Artificial Intelligence and Machine Learning - Part 4

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Artificial Intelligence and Machine Learning - Part 4

Question 1 of 25 Hints left: 3

Q1

What is 'Exploratory Data Analysis' (EDA)?

A Building predictive models
B Analyzing data to summarize its main characteristics and gain insights
C Evaluating model performance
D Reducing dimensionality

Q2

What is 'Time Series Analysis'?

A Analyzing data points collected or recorded at specific time intervals
B Performing clustering on data
C Classifying data into categories
D Reducing the number of features

Q3

What is the main advantage of using 'ensemble methods'?

A Increased model accuracy
B Reduced data preprocessing
C Simpler model interpretation
D Faster training times

Q4

What is 'Regularization' used for in machine learning models?

A To perform data imputation
B To prevent overfitting by adding a penalty for complexity
C To improve feature selection
D To increase model complexity

Q5

What is 'Naive Bayes' classifier based on?

A Decision trees
B k-Means Clustering
C Support Vector Machines (SVM)
D Bayes' Theorem with strong independence assumptions

Q6

What is 'Data Imputation'?

A Encoding categorical variables
B Scaling features
C Filling in missing values in a dataset
D Normalizing data

Q7

What does 'SVM' stand for?

A Statistical Vector Machine
B Supervised Vector Machine
C Sequential Vector Machine
D Support Vector Machine

Q8

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

A To improve model accuracy
B To preprocess the data
C To understand the contribution of each feature to model predictions
D To tune hyperparameters

Q9

What does 'support vector machine' (SVM) aim to find?

A The most important features
B The best data preprocessing method
C The most suitable model parameters
D The optimal hyperplane that separates classes

Q10

Which technique is used to assess the performance of regression models?

A ROC Curve
B Precision-Recall Curve
C Confusion Matrix
D Mean Squared Error (MSE)

Q11

What is 'Principal Component Analysis' (PCA) used for?

A Time series forecasting
B Anomaly detection
C Dimensionality reduction
D Feature scaling

Q12

What is 'Clustering'?

A The process of grouping similar data points together
B A method for feature scaling
C A technique for classification
D A method for regression analysis

Q13

What is 'CatBoost'?

A A method for dimensionality reduction
B A type of neural network
C A clustering algorithm
D A gradient boosting algorithm that handles categorical features effectively

Q14

What is 'Hierarchical Clustering'?

A A regression technique
B A clustering method that builds a hierarchy of clusters
C A method for feature extraction
D A technique for dimensionality reduction

Q15

What is 'Dropout' in neural networks?

A A technique for feature scaling
B A method for data normalization
C A regularization technique that randomly drops units during training to prevent overfitting
D A type of activation function

Q16

What is 'K-Fold Cross-Validation'?

A A technique for dimensionality reduction
B A technique for evaluating a model's performance by splitting data into k subsets
C A way to encode categorical variables
D A method for handling missing data

Q17

What is 'Model Ensemble'?

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

Q18

What is the main purpose of 'dimensionality reduction'?

A To normalize data
B To reduce the number of features
C To reduce the number of observations
D To increase model complexity

Q19

What is the purpose of the activation function in a neural network?

A To scale input features
B To initialize weights
C To introduce non-linearity in the model
D To improve training speed

Q20

What is the main purpose of 'feature scaling'?

A To perform dimensionality reduction
B To standardize feature ranges
C To create new features
D To handle missing values

Q21

What is 'Dropout'?

A A clustering method
B A feature scaling technique
C A regularization technique that randomly drops units from the neural network during training to prevent overfitting
D A type of dimensionality reduction

Q22

What is 'Recall' in classification metrics?

A The ratio of true positives to the sum of true positives and false negatives
B The ratio of true negatives to the sum of true negatives and false positives
C The ratio of true positives to the sum of true positives and false positives
D The ratio of false negatives to the sum of false negatives and true positives

Q23

What is the purpose of the 'AUC-ROC curve' in evaluating classification models?

A To assess the trade-off between true positive rate and false positive rate
B To evaluate feature importance
C To measure model accuracy
D To measure data scaling

Q24

What is 'K-Means Clustering'?

A A technique for classification
B A regression technique
C A partitioning method that divides data into K clusters based on similarity
D A method for feature scaling

Q25

What is the purpose of hyperparameter tuning in machine learning models?

A To optimize model performance
B To validate the model
C To train the model
D To select the best model

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