Solved MCQs on Artificial Intelligence Question: Which of the following is considered a subset of artificial intelligence? A...
Solved MCQs on Artificial Intelligence
- Question: Which of the following is considered a subset of artificial intelligence?
- A) Machine Learning
- B) Natural Language Processing
- C) Robotics
- D) All of the above
- Answer: D) All of the above
- Question: What is the primary goal of artificial intelligence?
- A) To create machines that can think and act like humans
- B) To automate tasks and improve efficiency
- C) To simulate human emotions in machines
- D) To replace humans in all tasks
- Answer: B) To automate tasks and improve efficiency
- Question: Which AI technique involves training algorithms to learn from data and make predictions or decisions?
- A) Expert Systems
- B) Natural Language Processing
- C) Machine Learning
- D) Neural Networks
- Answer: C) Machine Learning
- Question: Which type of machine learning algorithm is typically used for classification tasks?
- A) Reinforcement Learning
- B) Unsupervised Learning
- C) Supervised Learning
- D) Deep Learning
- Answer: C) Supervised Learning
- Question: What is the purpose of a neural network in artificial intelligence?
- A) To mimic the structure and function of the human brain
- B) To process natural language
- C) To generate random outputs
- D) To perform logical reasoning
- Answer: A) To mimic the structure and function of the human brain
- Question: Which of the following is an example of a chatbot using natural language processing?
- A) Siri
- B) Google Translate
- C) Alexa
- D) All of the above
- Answer: D) All of the above
- Question: What is the term for AI systems that can improve their performance over time through experience?
- A) Artificial Neural Networks
- B) Deep Learning
- C) Reinforcement Learning
- D) Genetic Algorithms
- Answer: C) Reinforcement Learning
- Question: Which AI application involves teaching a computer to recognize patterns in images or videos?
- A) Optical Character Recognition (OCR)
- B) Speech Recognition
- C) Computer Vision
- D) Sentiment Analysis
- Answer: C) Computer Vision
- Question: What is the main advantage of using genetic algorithms in AI?
- A) They are highly interpretable
- B) They require large amounts of labeled data
- C) They can find optimal solutions in complex search spaces
- D) They are computationally efficient
- Answer: C) They can find optimal solutions in complex search spaces
- Question: Which of the following is NOT a potential ethical concern related to artificial intelligence?
- A) Job displacement
- B) Bias in algorithms
- C) Autonomous weapons
- D) Limited computing power
- Answer: D) Limited computing power
- estion: What is the process of feeding labeled data to a machine learning algorithm known as?
- A) Unsupervised learning
- B) Reinforcement learning
- C) Supervised learning
- D) Deep learning
- Answer: C) Supervised learning
- Question: Which of the following is NOT a type of machine learning algorithm?
- A) Decision Trees
- B) K-Means Clustering
- C) Gradient Descent
- D) Artificial Neural Networks
- Answer: C) Gradient Descent
- Question: Which AI technique involves mimicking the behavior of ants, bees, or other social organisms to solve optimization problems?
- A) Swarm Intelligence
- B) Genetic Algorithms
- C) Reinforcement Learning
- D) Expert Systems
- Answer: A) Swarm Intelligence
- Question: What is the term for AI systems that can understand, interpret, and generate human-like text?
- A) Natural Language Processing
- B) Machine Learning
- C) Expert Systems
- D) Speech Recognition
- Answer: A) Natural Language Processing
- Question: Which of the following is an example of unsupervised learning?
- A) Predicting house prices based on historical data
- B) Sorting emails into spam and non-spam folders
- C) Grouping customers based on purchasing behavior
- D) Playing chess against a computer opponent
- Answer: C) Grouping customers based on purchasing behavior
- Question: What is the main advantage of using deep learning over traditional machine learning algorithms?
- A) Deep learning requires less computational power
- B) Deep learning models are more interpretable
- C) Deep learning can automatically learn hierarchical representations of data
- D) Deep learning is less prone to overfitting
- Answer: C) Deep learning can automatically learn hierarchical representations of data
- Question: Which of the following is NOT a characteristic of artificial intelligence?
- A) Creativity
- B) Adaptability
- C) Emotion
- D) Consistency
- Answer: C) Emotion
- Question: What is the term for the ability of an AI system to perform tasks that require human intelligence?
- A) Artificial Consciousness
- B) Artificial General Intelligence
- C) Artificial Superintelligence
- D) Artificial Narrow Intelligence
- Answer: B) Artificial General Intelligence
- Question: Which of the following is a potential application of reinforcement learning?
- A) Speech Recognition
- B) Autonomous Driving
- C) Image Classification
- D) Text Translation
- Answer: B) Autonomous Driving
- Question: What is the term for AI systems that can perceive and understand the physical world through sensors and actuators?
- A) Embodied AI
- B) Symbolic AI
- C) Strong AI
- D) Weak AI
- Answer: A) Embodied AI
- Question: Which of the following is NOT a component of an artificial neural network?
- A) Neurons
- B) Weights
- C) Loss Function
- D) Rules
- Answer: D) Rules
- Question: Which of the following is NOT a challenge in training deep learning models?
- A) Vanishing Gradient Problem
- B) Overfitting
- C) Underfitting
- D) Lack of Data
- Answer: D) Lack of Data
- Question: What is the term for AI systems that can understand and interpret human speech?
- A) Natural Language Processing
- B) Speech Recognition
- C) Sentiment Analysis
- D) Optical Character Recognition
- Answer: B) Speech Recognition
- Question: Which AI technique involves deriving rules or logical conclusions from a knowledge base?
- A) Reinforcement Learning
- B) Genetic Algorithms
- C) Expert Systems
- D) Deep Learning
- Answer: C) Expert Systems
- Question: What is the term for AI systems that can generate new content, such as text, images, or music?
- A) Natural Language Processing
- B) Generative Adversarial Networks
- C) Reinforcement Learning
- D) Convolutional Neural Networks
- Answer: B) Generative Adversarial Networks
- Question: Which of the following is NOT a category of machine learning algorithms?
- A) Supervised Learning
- B) Unsupervised Learning
- C) Reinforcement Learning
- D) Deterministic Learning
- Answer: D) Deterministic Learning
- Question: What is the term for the ability of an AI system to learn and improve from experience without explicit programming?
- A) Machine Learning
- B) Artificial Intelligence
- C) Deep Learning
- D) Cognitive Computing
- Answer: A) Machine Learning
- Question: Which of the following is an example of a natural language processing task?
- A) Predicting stock prices
- B) Recognizing objects in images
- C) Translating text from one language to another
- D) Playing chess against a computer opponent
- Answer: C) Translating text from one language to another
- Question: What is the term for AI systems that can understand and respond to human emotions?
- A) Emotion AI
- B) Sentiment Analysis
- C) Cognitive Computing
- D) Affective Computing
- Answer: D) Affective Computing
- Question: Which of the following is NOT a step in the machine learning process?
- A) Data Preprocessing
- B) Model Evaluation
- C) Model Training
- D) Model Compilation
- Answer: D) Model Compilation
- Question: What is the term for AI systems that can simulate human-like conversation with users?
- A) Chatbots
- B) Virtual Assistants
- C) Cognitive Agents
- D) All of the above
- Answer: D) All of the above
- Question: Which of the following is an example of a reinforcement learning application?
- A) Image Classification
- B) Playing Chess
- C) Language Translation
- D) Speech Recognition
- Answer: B) Playing Chess
- Question: What is the term for the process of converting unstructured text data into a structured format for analysis?
- A) Sentiment Analysis
- B) Text Mining
- C) Data Wrangling
- D) Natural Language Processing
- Answer: D) Natural Language Processing
- Question: Which of the following is NOT a type of neural network architecture?
- A) Convolutional Neural Network (CNN)
- B) Recurrent Neural Network (RNN)
- C) Support Vector Machine (SVM)
- D) Long Short-Term Memory (LSTM)
- Answer: C) Support Vector Machine (SVM)
- Question: What is the term for the process of automatically generating insights and predictions from large datasets?
- A) Machine Learning
- B) Predictive Analytics
- C) Data Mining
- D) Big Data Analysis
- Answer: B) Predictive Analytics
- Question: Which of the following is NOT a common machine learning algorithm?
- A) Linear Regression
- B) K-Nearest Neighbors (KNN)
- C) Breadth-First Search (BFS)
- D) Decision Trees
- Answer: C) Breadth-First Search (BFS)
- Question: What is the term for the process of adjusting the parameters of a machine learning model to minimize errors?
- A) Model Evaluation
- B) Model Selection
- C) Model Training
- D) Model Validation
- Answer: C) Model Training
- Question: Which of the following is NOT a limitation of artificial intelligence?
- A) Lack of Creativity
- B) Limited Computational Power
- C) Ethical Concerns
- D) Emotionless Decision Making
- Answer: B) Limited Computational Power
- Question: Which of the following is an example of a supervised learning algorithm?
- A) K-Means Clustering
- B) Support Vector Machine (SVM)
- C) K-Nearest Neighbors (KNN)
- D) Apriori Algorithm
- Answer: B) Support Vector Machine (SVM)
- Question: What is the term for the process of automatically discovering patterns and insights from data?
- A) Predictive Modeling
- B) Machine Learning
- C) Data Mining
- D) Pattern Recognition
- Answer: C) Data Mining
- Question: Which of the following is an example of a clustering algorithm?
- A) Decision Trees
- B) K-Means Clustering
- C) Linear Regression
- D) Random Forest
- Answer: B) K-Means Clustering
- Question: What is the term for the process of determining the most appropriate action to take in a given situation?
- A) Decision Making
- B) Policy Evaluation
- C) Reinforcement Learning
- D) Optimization
- Answer: A) Decision Making
- Question: Which of the following is an example of an unsupervised learning algorithm?
- A) Logistic Regression
- B) K-Means Clustering
- C) Support Vector Machine (SVM)
- D) Random Forest
- Answer: B) K-Means Clustering
- Question: What is the term for the process of converting spoken language into text?
- A) Speech Recognition
- B) Text Mining
- C) Natural Language Processing
- D) Optical Character Recognition
- Answer: A) Speech Recognition
- Question: Which of the following is NOT a component of reinforcement learning?
- A) Agent
- B) Environment
- C) Supervisor
- D) Reward Signal
- Answer: C) Supervisor
- Question: What is the term for the process of training a machine learning model on a subset of the data and then evaluating its performance on another subset?
- A) Cross-Validation
- B) Feature Engineering
- C) Hyperparameter Tuning
- D) Ensemble Learning
- Answer: A) Cross-Validation
- Question: Which of the following is NOT a type of deep learning architecture?
- A) Recurrent Neural Network (RNN)
- B) Convolutional Neural Network (CNN)
- C) Long Short-Term Memory (LSTM)
- D) Decision Tree
- Answer: D) Decision Tree
- Question: What is the term for the process of automatically generating new examples of data from an existing dataset?
- A) Data Sampling
- B) Data Augmentation
- C) Data Cleansing
- D) Data Imputation
- Answer: B) Data Augmentation
- Question: Which of the following is an example of a generative model in machine learning?
- A) Logistic Regression
- B) K-Means Clustering
- C) Autoencoder
- D) Support Vector Machine (SVM)
- Answer: C) Autoencoder
- Question: What is the term for the process of converting handwritten text into machine-readable text?
- A) Optical Character Recognition (OCR)
- B) Natural Language Processing (NLP)
- C) Speech Recognition
- D) Sentiment Analysis
- Answer: A) Optical Character Recognition (OCR)
- Question: Which of the following is NOT a type of reinforcement learning algorithm?
- A) Q-Learning
- B) Deep Q-Network (DQN)
- C) Random Forest
- D) Policy Gradient Methods
- Answer: C) Random Forest
- Question: What is the term for the process of identifying and extracting useful patterns and information from large datasets?
- A) Data Visualization
- B) Data Cleaning
- C) Data Mining
- D) Data Compression
- Answer: C) Data Mining
- Question: Which of the following is a common evaluation metric for classification problems in machine learning?
- A) Mean Squared Error (MSE)
- B) Accuracy
- C) Root Mean Squared Error (RMSE)
- D) R-Squared
- Answer: B) Accuracy
- Question: What is the term for the process of selecting the most relevant features or variables for a machine learning model?
- A) Feature Engineering
- B) Feature Selection
- C) Feature Extraction
- D) Feature Scaling
- Answer: B) Feature Selection
- Question: Which of the following is an example of a semi-supervised learning algorithm?
- A) K-Means Clustering
- B) Decision Trees
- C) Support Vector Machine (SVM)
- D) Linear Regression
- Answer: A) K-Means Clustering
- Question: What is the term for the process of combining multiple machine learning models to improve predictive performance?
- A) Model Selection
- B) Model Evaluation
- C) Ensemble Learning
- D) Hyperparameter Tuning
- Answer: C) Ensemble Learning
- Question: Which of the following is a limitation of unsupervised learning?
- A) Requires labeled data for training
- B) Can be computationally expensive
- C) May produce inaccurate results due to lack of supervision
- D) Limited scalability to large datasets
- Answer: C) May produce inaccurate results due to lack of supervision
- Question: What is the term for the process of transforming raw data into a format suitable for analysis?
- A) Data Cleansing
- B) Data Preprocessing
- C) Data Wrangling
- D) Data Engineering
- Answer: B) Data Preprocessing
- Question: Which of the following is an example of a dimensionality reduction technique?
- A) Principal Component Analysis (PCA)
- B) Linear Regression
- C) Logistic Regression
- D) K-Means Clustering
- Answer: A) Principal Component Analysis (PCA)
- Question: What is the term for the process of searching for the best hyperparameters for a machine learning model?
- A) Model Selection
- B) Model Evaluation
- C) Hyperparameter Tuning
- D) Ensemble Learning
- Answer: C) Hyperparameter Tuning
- Question: Which of the following is NOT a common approach to feature engineering?
- A) One-Hot Encoding
- B) Principal Component Analysis (PCA)
- C) Polynomial Features
- D) Feature Scaling
- Answer: B) Principal Component Analysis (PCA)
- Question: What is the term for the process of assessing the performance of a machine learning model on unseen data?
- A) Model Selection
- B) Model Evaluation
- C) Model Training
- D) Model Validation
- Answer: B) Model Evaluation
- Question: Which of the following is a common technique for handling missing data in machine learning?
- A) Imputation
- B) Transformation
- C) Normalization
- D) Encoding
- Answer: A) Imputation
- Question: What is the term for the process of automatically generating new features from existing ones to improve model performance?
- A) Feature Engineering
- B) Feature Selection
- C) Feature Extraction
- D) Feature Scaling
- Answer: A) Feature Engineering
- Question: Which of the following is a common approach to handling categorical variables in machine learning?
- A) One-Hot Encoding
- B) Standardization
- C) Min-Max Scaling
- D) Normalization
- Answer: A) One-Hot Encoding
- Question: What is the term for the process of dividing a dataset into separate training and testing subsets?
- A) Data Partitioning
- B) Data Splitting
- C) Data Sampling
- D) Data Preprocessing
- Answer: A) Data Partitioning
- Question: Which of the following is NOT a common method of model evaluation in machine learning?
- A) Accuracy
- B) Precision
- C) Recall
- D) Loss Function
- Answer: D) Loss Function
- Question: What is the term for the process of transforming numerical variables to a common scale?
- A) Feature Engineering
- B) Feature Scaling
- C) Feature Selection
- D) Feature Extraction
- Answer: B) Feature Scaling
- Question: Which of the following is a common technique for feature selection in machine learning?
- A) Principal Component Analysis (PCA)
- B) Recursive Feature Elimination (RFE)
- C) One-Hot Encoding
- D) Polynomial Features
- Answer: B) Recursive Feature Elimination (RFE)
- Question: What is the term for the process of assessing the performance of a machine learning model during training?
- A) Model Selection
- B) Model Evaluation
- C) Model Training
- D) Model Validation
- Answer: D) Model Validation
- Question: Which of the following is a common technique for reducing overfitting in machine learning models?
- A) Increasing the model complexity
- B) Decreasing the amount of training data
- C) Adding more features to the model
- D) Regularization
- Answer: D) Regularization
- Question: What is the term for the process of adjusting the learning rate during training to optimize model performance?
- A) Learning Rate Optimization
- B) Gradient Descent
- C) Hyperparameter Tuning
- D) Stochastic Gradient Descent
- Answer: A) Learning Rate Optimization
- Question: Which of the following is a common approach to reducing bias in machine learning models?
- A) Increasing the model complexity
- B) Decreasing the learning rate
- C) Increasing the amount of training data
- D) Data Augmentation
- Answer: C) Increasing the amount of training data
- Question: What is the term for the process of identifying and removing outliers from a dataset?
- A) Outlier Detection
- B) Outlier Removal
- C) Outlier Handling
- D) Outlier Analysis
- Answer: A) Outlier Detection
- Question: Which of the following is a common approach to handling imbalanced classes in machine learning?
- A) Overfitting
- B) Oversampling
- C) Underfitting
- D) Feature Scaling
- Answer: B) Oversampling
- Question: What is the term for the process of training a machine learning model multiple times with different subsets of the data?
- A) Ensemble Learning
- B) Cross-Validation
- C) Hyperparameter Tuning
- D) Stochastic Gradient Descent
- Answer: B) Cross-Validation
- Question: Which of the following is a common approach to measuring the uncertainty of a machine learning model's predictions?
- A) Confidence Interval
- B) F1 Score
- C) Mean Absolute Error (MAE)
- D) R-Squared
- Answer: A) Confidence Interval
- Question: What is the term for the process of using multiple machine learning models to make predictions?
- A) Model Stacking
- B) Model Fusion
- C) Model Ensemble
- D) Model Integration
- Answer: C) Model Ensemble
- Question: Which of the following is NOT a common approach to model ensembling?
- A) Bagging
- B) Boosting
- C) Stacking
- D) Clustering
- Answer: D) Clustering
- Question: What is the term for the process of automatically tuning the hyperparameters of a machine learning model?
- A) Model Selection
- B) Model Evaluation
- C) Hyperparameter Tuning
- D) Ensemble Learning
- Answer: C) Hyperparameter Tuning
- Question: Which of the following is a common approach to hyperparameter tuning?
- A) Grid Search
- B) Random Search
- C) Bayesian Optimization
- D) All of the above
- Answer: D) All of the above
- Question: What is the term for the process of assessing the generalization error of a machine learning model on new, unseen data?
- A) Model Evaluation
- B) Model Selection
- C) Model Validation
- D) Model Testing
- Answer: C) Model Validation
- Question: Which of the following is NOT a common approach to model evaluation?
- A) Cross-Validation
- B) Train-Test Split
- C) Validation Set
- D) Precision-Recall Curve
- Answer: D) Precision-Recall Curve
- Question: What is the term for the process of selecting the best machine learning model for a given task?
- A) Model Training
- B) Model Evaluation
- C) Model Selection
- D) Model Validation
- Answer: C) Model Selection
- Question: Which of the following is a common technique for model selection?
- A) Grid Search
- B) Random Search
- C) Cross-Validation
- D) All of the above
- Answer: D) All of the above
- Question: What is the term for the process of adjusting the parameters of a machine learning model to minimize its error on the training data?
- A) Model Evaluation
- B) Model Selection
- C) Model Training
- D) Model Validation
- Answer: C) Model Training
- Question: Which of the following is NOT a common approach to model training?
- A) Gradient Descent
- B) Stochastic Gradient Descent
- C) Reinforcement Learning
- D) Backpropagation
- Answer: C) Reinforcement Learning
- Question: What is the term for the process of updating the parameters of a neural network to minimize its error on the training data?
- A) Gradient Descent
- B) Stochastic Gradient Descent
- C) Backpropagation
- D) Cross-Validation
- Answer: C) Backpropagation
- Question: Which of the following is NOT a common activation function in neural networks?
- A) Sigmoid
- B) ReLU
- C) Tanh
- D) Logistic
- Answer: D) Logistic
- Question: What is the term for the process of propagating errors backward through a neural network to update its parameters?
- A) Gradient Descent
- B) Stochastic Gradient Descent
- C) Backpropagation
- D) Cross-Validation
- Answer: C) Backpropagation
- Question: Which of the following is NOT a type of machine learning problem?
- A) Classification
- B) Regression
- C) Clustering
- D) Sorting
- Answer: D) Sorting
- Question: What is the term for the process of reducing the size of a neural network to improve its efficiency?
- A) Model Pruning
- B) Model Compression
- C) Model Optimization
- D) Model Shrinking
- Answer: A) Model Pruning
- Question: Which of the following is a common method for training deep learning models on large datasets?
- A) Stochastic Gradient Descent
- B) Batch Gradient Descent
- C) Mini-batch Gradient Descent
- D) All of the above
- Answer: D) All of the above
- Question: What is the term for the process of generating new examples of data by combining existing examples?
- A) Data Augmentation
- B) Data Cleansing
- C) Data Sampling
- D) Data Compression
- Answer: A) Data Augmentation
- Question: Which of the following is NOT a common technique for reducing the dimensionality of data?
- A) Principal Component Analysis (PCA)
- B) Singular Value Decomposition (SVD)
- C) Feature Scaling
- D) t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Answer: C) Feature Scaling
- Question: What is the term for the process of transforming categorical variables into numerical ones?
- A) One-Hot Encoding
- B) Label Encoding
- C) Ordinal Encoding
- D) Target Encoding
- Answer: A) One-Hot Encoding
- Question: Which of the following is a common technique for handling imbalanced datasets in machine learning?
- A) Random Undersampling
- B) Random Oversampling
- C) SMOTE (Synthetic Minority Over-sampling Technique)
- D) All of the above
- Answer: D) All of the above
- Question: What is the term for the process of evaluating the performance of a machine learning model on new, unseen data?
- A) Model Testing
- B) Model Validation
- C) Model Evaluation
- D) Model Selection
- Answer: A) Model Testing
- Question: Which of the following is NOT a common metric for evaluating classification models?
- A) Accuracy
- B) Mean Squared Error (MSE)
- C) Precision
- D) Recall
- Answer: B) Mean Squared Error (MSE)
- Question: What is the term for the process of tuning hyperparameters to improve the performance of a machine learning model?- A) Hyperparameter Optimization- B) Hyperparameter Tuning- C) Hyperparameter Adjustment- D) Hyperparameter Selection- Answer: B) Hyperparameter Tuning
- Question: Which of the following is a common technique for selecting the best hyperparameters for a machine learning model?- A) Grid Search- B) Random Search- C) Bayesian Optimization- D) All of the above- Answer: D) All of the above
- Question: What is the term for the process of selecting the most important features for a machine learning model?- A) Feature Engineering- B) Feature Selection- C) Feature Extraction- D) Feature Scaling- Answer: B) Feature Selection
- Question: Which of the following is NOT a common technique for feature selection?- A) Principal Component Analysis (PCA)- B) Recursive Feature Elimination (RFE)- C) Lasso Regression- D) K-Means Clustering- Answer: D) K-Means Clustering
- Question: What is the term for the process of splitting a dataset into multiple subsets for training, validation, and testing?- A) Data Partitioning- B) Data Splitting- C) Data Sampling- D) Data Preprocessing- Answer: A) Data Partitioning
- Question: Which of the following is NOT a common approach to splitting a dataset?- A) Train-Test Split- B) Validation Split- C) Cross-Validation- D) Random Sampling- Answer: D) Random Sampling
- Question: What is the term for the process of adjusting the learning rate during training to optimize model performance?- A) Learning Rate Optimization- B) Gradient Descent- C) Hyperparameter Tuning- D) Stochastic Gradient Descent- Answer: A) Learning Rate Optimization
- Question: Which of the following is a common technique for reducing overfitting in machine learning models?- A) Regularization- B) Data Augmentation- C) Dropout- D) All of the above- Answer: D) All of the above
- Question: What is the term for the process of preventing a machine learning model from becoming too complex?- A) Overfitting- B) Underfitting- C) Regularization- D) Generalization- Answer: C) Regularization
- Question: Which of the following is NOT a common regularization technique?- A) L1 Regularization- B) L2 Regularization- C) Dropout- D) Batch Normalization- Answer: D) Batch Normalization
- Question: What is the term for the process of updating the parameters of a neural network to minimize its error on the training data?- A) Gradient Descent- B) Stochastic Gradient Descent- C) Backpropagation- D) Cross-Validation- Answer: C) Backpropagation
- Question: Which of the following is NOT a common optimization algorithm used in training neural networks?- A) Gradient Descent- B) Stochastic Gradient Descent- C) Adam- D) Naive Bayes- Answer: D) Naive Bayes
- Question: What is the term for the process of propagating errors backward through a neural network to update its parameters?- A) Gradient Descent- B) Stochastic Gradient Descent- C) Backpropagation- D) Cross-Validation- Answer: C) Backpropagation
- Question: Which of the following is NOT a common activation function used in neural networks?- A) Sigmoid- B) ReLU- C) Tanh- D) Logistic Regression- Answer: D) Logistic Regression
- Question: What is the term for the process of updating the weights of a neural network to minimize its error on the training data?- A) Weight Optimization- B) Weight Adjustment- C) Weight Updating- D) Weight Training- Answer: C) Weight Updating
- Question: Which of the following is NOT a common type of neural network architecture?- A) Feedforward Neural Network- B) Recurrent Neural Network- C) Convolutional Neural Network- D) Decision Tree- Answer: D) Decision Tree
- Question: What is the term for the process of training a neural network on a subset of the data and then evaluating its performance on another subset?- A) Cross-Validation- B) Train-Test Split- C) Model Validation- D) Hyperparameter Tuning- Answer: B) Train-Test Split
- Question: Which of the following is NOT a common approach to model evaluation?- A) Cross-Validation- B) Train-Test Split- C) Validation Set- D) Regularization- Answer: D) Regularization
- Question: What is the term for the process of assessing the performance of a machine learning model on new, unseen data?- A) Model Testing- B) Model Validation- C) Model Evaluation- D) Model Selection- Answer: A) Model Testing
- Question: Which of the following is NOT a common metric for evaluating regression models?- A) Accuracy- B) Mean Squared Error (MSE)- C) Mean Absolute Error (MAE)- D) R-Squared- Answer: A) Accuracy
- Question: What is the term for the process of selecting the best machine learning model for a given task?- A) Model Training- B) Model Evaluation- C) Model Selection- D) Model Validation- Answer: C) Model Selection
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