Quiz | Croma Campus

What is the purpose of dimensionality reduction in machine learning?

To increase the complexity of the model
To reduce overfitting
To decrease computational efficiency
To increase computational efficiency

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What is the primary goal of Artificial Intelligence (AI)?

To replace human intelligence
To replicate human intelligence in machines
To automate all human tasks
To develop intelligent systems that can perform tasks that typically require human intelligence

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Which technique is used to handle imbalanced classes in a classification problem?

Oversampling
Undersampling
SMOTE
All of the above

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Which of the following is NOT a component of an expert system?

Knowledge Base
Inference Engine
User Interface
Deep Learning

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What is the main drawback of oversampling techniques?

Increased risk of overfitting
Decreased computational efficiency
Loss of information
Sensitivity to outliers

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Which technique is used to reduce the dimensionality of data while preserving its variance?

Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Singular Value Decomposition (SVD)
All of the above

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Which technique is used to evaluate the performance of a machine learning model?

Precision
Recall
F1 Score
All of the above

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Which type of validation technique involves dividing the dataset into k equal-sized subsets?

Holdout Validation
Cross-Validation
Leave-One-Out Validation
Random Sampling

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Which algorithm is used to find frequent itemsets in a transactional database?

Apriori
K-Means
DBSCAN
Hierarchical Clustering

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What is the main advantage of ensemble learning algorithms?

Reduced computational complexity
Improved predictive performance
Increased interpretability
Lower risk of overfitting

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What is the main objective of supervised learning?

To learn from unlabeled data
To learn from labeled data
To learn from reinforcement
To learn from human experts

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Which technique is used to make decisions in uncertain and complex environments?

Fuzzy Logic
Genetic Algorithms
Reinforcement Learning
Bayesian Networks

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Which algorithm is commonly used for classification tasks in Machine Learning?

K-Means
Gradient Descent
Support Vector Machines (SVM)
Apriori

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Which technique is used to generate human-like outputs from a computer program?

Expert Systems
Genetic Algorithms
Natural Language Generation (NLG)
Reinforcement Learning

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Which of the following is NOT a subfield of AI?

Natural Language Processing (NLP)
Robotics
Calculus
Machine Learning

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Which technique is used to combine the predictions of multiple weak learners to build a strong learner?

Bagging
Boosting
Stacking
All of the above

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What is the process of extracting meaningful information from text called?

Text Mining
Text Understanding
Information Retrieval
Text Analysis

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What is the primary objective of Artificial Intelligence (AI)?

To replace human intelligence
To mimic human intelligence in machines
To develop systems that can perform tasks requiring human intelligence
To automate all human tasks

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Which technique is used to evaluate the performance of a classification model across different thresholds?

Receiver Operating Characteristic (ROC) Curve
Precision-Recall Curve
Confusion Matrix
All of the above

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Which algorithm is used to find the shortest path in a graph?

Breadth-First Search (BFS)
Depth-First Search (DFS)
Dijkstra's Algorithm
A* Algorithm

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Which technique is used to handle missing data in a dataset?

Mean Imputation
Median Imputation
Mode Imputation
All of the above

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Which approach in AI focuses on mimicking the evolutionary process to solve complex problems?

Expert Systems
Genetic Algorithms
Fuzzy Logic
Bayesian Networks

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What is the term used to describe the difference between predicted values and actual values in regression analysis?

Error
Loss
Residual
Deviance

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What is the main purpose of a confusion matrix?

To visualize the performance of a classification model
To calculate the accuracy of a regression model
To estimate the confidence interval of a prediction
To measure the variance of a model

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

To increase computational efficiency
To reduce overfitting
To introduce non-linearity
To normalize input data

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Which of the following is NOT a characteristic of an artificial neural network?

Learning
Adaptability
Fuzzy Logic
Generalization

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Which technique is used to understand and interpret human language by computers?

Sentiment Analysis
Natural Language Processing (NLP)
Machine Translation
Optical Character Recognition (OCR)

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Which technique in AI allows computers to learn from experience and understand patterns in data without being explicitly programmed?

Natural Language Processing (NLP)
Expert Systems
Machine Learning
Neural Networks

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Which technique is used to evaluate the importance of each feature in a machine learning model?

Feature Selection
Feature Extraction
Feature Engineering
All of the above

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Which of the following is an unsupervised learning algorithm?

Decision Trees
K-Means
Naive Bayes
Random Forest

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