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Categories / Computer Science & IT / Artificial Intelligence and Machine Learning / Artificial Intelligence and Machine Learning - Part 5

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Artificial Intelligence and Machine Learning - Part 5

Question 1 of 25 Hints left: 3

Q1

Which algorithm is typically used for anomaly detection?

A k-Means Clustering
B Isolation Forest
C Random Forest
D Linear Regression

Q2

What is 'Dimensionality Reduction'?

A Normalizing the features
B Reducing the number of features in a dataset while retaining important information
C Increasing the number of features
D Splitting data into training and testing sets

Q3

What is 'Overfitting' in machine learning?

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

Q4

What does the term 'cross-validation' refer to?

A A process to optimize hyperparameters
B A method to combine different models
C A method to split the dataset
D A technique to evaluate model performance

Q5

What is 'Hierarchical Temporal Memory' (HTM)?

A A clustering algorithm
B A theoretical model of the neocortex for understanding hierarchical learning
C A type of regression model
D A method for feature scaling

Q6

What does 'Epoch' represent in training neural networks?

A A single iteration of training
B A specific type of neural network layer
C One complete pass through the entire training dataset
D A method for data preprocessing

Q7

What is the main goal of 'model selection'?

A To preprocess data
B To select important features
C To find the best model for a given task
D To split data into subsets

Q8

What is 'Mean Absolute Error' (MAE)?

A The average of absolute differences between predicted and actual values
B The sum of absolute differences
C The average squared differences between predicted and actual values
D The ratio of true positives to total instances

Q9

What is 'Underfitting' in machine learning?

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

Q10

What is the purpose of 'data imputation' in data preprocessing?

A To scale data
B To replace missing values
C To split data into training and testing sets
D To extract features

Q11

What is 'Principal Component Analysis' (PCA)?

A A technique for reducing the dimensionality of data by transforming features into principal components
B A method for handling missing values
C A technique for classification
D A form of data normalization

Q12

What is 'Time Series Forecasting'?

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

Q13

What does 'Outlier' mean in data analysis?

A A data point that differs significantly from other observations
B A data point that is similar to other observations
C A redundant feature
D A missing value in the dataset

Q14

What technique is used to reduce overfitting in machine learning models?

A Data Normalization
B Data Augmentation
C Regularization
D Feature Scaling

Q15

Which method is used to detect outliers in a dataset?

A k-Means Clustering
B Random Forest
C Principal Component Analysis (PCA)
D Isolation Forest

Q16

What does 'ensemble learning' refer to?

A Evaluating models using cross-validation
B Training models on different datasets
C Combining multiple models to improve performance
D Reducing the number of features

Q17

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

A Average Uniform Classification
B Aggregate Utility Curve
C Absolute Uncertainty Calculation
D Area Under the Curve

Q18

What is 'Precision' in classification metrics?

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

Q19

What is a key characteristic of 'supervised learning'?

A It uses labeled data
B It involves clustering similar data
C It uses unlabeled data
D It performs dimensionality reduction

Q20

What is 'Supervised Learning'?

A A type of machine learning with unlabeled data
B A method for dimensionality reduction
C A technique for clustering data
D A type of machine learning where the model is trained on labeled data

Q21

What is the main goal of clustering algorithms?

A Feature Extraction
B Dimensionality Reduction
C Grouping similar data
D Classification

Q22

What is the purpose of 'data normalization'?

A To reduce the number of features
B To combine different datasets
C To scale data to a common range
D To increase data variance

Q23

What is 'L2 Regularization'?

A A technique for clustering
B A method for feature scaling
C A type of neural network
D A regularization technique that adds the squared value of the coefficients to the loss function

Q24

What is 'Outlier Detection'?

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

Q25

What is 'Variance in Machine Learning'?

A Systematic error in predictions
B Error due to dimensionality reduction
C Error due to data scaling
D The variability of model predictions for a given data point

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