Machine Learning MCQ with Answers

  1. What is machine learning?
  2. Machine Learning is a field of AI consisting of learning algorithms that ..............
  3. .............. is a widely used and effective machine learning algorithm based on the idea of bagging.
  4. What is the disadvantage of decision trees?
  5. How can you handle missing or corrupted data in a dataset?
  6. Which of the followings are most widely used metrics and tools to assess a classification model?
  7. Machine learning algorithms build a model based on sample data, known as .................
  8. Machine learning is a subset of ................
  9. A Machine Learning technique that helps in detecting the outliers in data.
  10. Who is the father of Machine Learning?
  11. What is the most significant phase in a genetic algorithm?
  12. Which one in the following is not Machine Learning disciplines?
  13. Machine Learning has various function representation, which of the following is not function of symbolic?
  14. ................... algorithms enable the computers to learn from data, and even improve themselves, without being explicitly programmed.
Machine Learning MCQ

Machine learning is a field of computer science that deals with the problem of finding mathematical and statistical functions that best explain the relationship between input data, output data, and other inputs (external) to a system. Machine learning has some uses in areas such as detection, recommendation systems, fraud detection, machine translation, visual recognition, and the development of autonomous robotic systems.

Finally, Take the Machine Learning MCQ Test and identify your strengths in machine learning. It will help you to understand the ML concepts well & improve your knowledge.

  • The selective acquisition of knowledge through the use of manual programs
  • The selective acquisition of knowledge through the use of computer programs
  • The autonomous acquisition of knowledge through the use of manual programs
  • The autonomous acquisition of knowledge through the use of computer programs
View Answer
  • At executing some task
  • Over time with experience
  • Improve their performance
  • All of the above
View Answer
  • Regression
  • Classification
  • Decision Tree
  • Random Forest
View Answer
  • Factor analysis
  • Decision trees are robust to outliers
  • Decision trees are prone to be overfit
  • All of the above
View Answer
  • Drop missing rows or columns
  • Assign a unique category to missing values
  • Replace missing values with mean/median/mode
  • All of the above
Download Free : Machine Learning MCQ PDF
View Answer
  • Confusion matrix
  • Cost-sensitive accuracy
  • Area under the ROC curve
  • All of the above
View Answer
  • Training Data
  • Transfer Data
  • Data Training
  • None of the above
View Answer
  • Deep Learning
  • Artificial Intelligence
  • Data Learining
  • None of the above
View Answer
  • Clustering
  • Classification
  • Anamoly Detection
  • All of the above
View Answer
  • Geoffrey Hill
  • Geoffrey Chaucer
  • Geoffrey Everest Hinton
  • None of the above
View Answer
  • Selection
  • Mutation
  • Crossover
  • Fitness function
View Answer
  • Physics
  • Information Theory
  • Neurostatistics
  • Optimization Control
View Answer
  • Decision Trees
  • Rules in propotional Logic
  • Rules in first-order predicate logic
  • Hidden-Markov Models (HMM)
View Answer
  • Deep Learning
  • Machine Learning
  • Artificial Intelligence
  • None of the above
View Answer
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • All of the above
View Answer
  • PCA
  • Naive Bayesian
  • Linear Regression
  • Decision Tree Answer
View Answer
  • Reinforcement Learning
  • Supervised Learning: Classification
  • Unsupervised Learning: Regression
  • None of the above
View Answer
  • Case-based
  • Neural Network
  • Linear Regression
  • Support Vector Machines
View Answer
  • Clustering
  • Regression
  • Classification
  • All of the above
View Answer
  • K-Means clustering
  • Conceptual clustering
  • Agglomerative clustering
  • All of the above
View Answer
  • Ignored
  • Treated as equal compares
  • Treated as unequal compares.
  • Replaced with a default value.
View Answer
  • Bayes classifier
  • Linear regression
  • Ogistic regression
  • None of the above
View Answer
  • Linear, binary
  • Linear, numeric
  • Nonlinear, binary
  • Nonlinear, numeric
View Answer
  • Linear
  • Nonlinear
  • Categorical
  • None of the above
View Answer
  • Mean relative error
  • Mean squared error
  • Mean absolute error
  • Root mean squared error
View Answer
  • Test
  • Training
  • Validation
  • None of the above
View Answer
  • True
  • False
View Answer
  • Mean positive error
  • Mean absolute error
  • Mean squared error
  • Root mean squared error
View Answer
  • The output attribute must be numeric.
  • The output attribute must be categorical
  • The resultant model is designed to determine future outcomes
  • The resultant model is designed to classify current behavior.
View Answer
  • Predictive variable
  • Estimated variable
  • Dependent variable
  • Independent variable
View Answer
  • Input attribute
  • Output attribute
  • Hidden attribute
  • Categorical attribute
View Answer
  • SVG
  • SVM
  • Random forest
  • All of the above
View Answer
  • Physiscs
  • Information theory
  • Nuero Statistics
  • None of the above
View Answer
  • Probably Approx Cost
  • Probably Approximate Correct
  • Probability Approx Communication
  • None of the above
View Answer
  • Statistical learning theory
  • Computational learning theory
  • Both Statistical & Computational learning theory
  • None of the above
View Answer
  • Case-based
  • Neural Network
  • Linear regression
  • All of true
View Answer
  • Learning to recognize spoken words
  • Learning to drive an autonomous vehicle
  • Learning to classify new astronomical structures
  • All of the above
View Answer
  • Data mining
  • Internet of things
  • Artificial intelligence
  • None of the above
View Answer
  • True
  • False
View Answer
  • Fast
  • Accuracy
  • Scalable
  • All of the above
View Answer
  • Null
  • Accuracy
  • Machine learning model
  • Machine learning algorithm
View Answer
  • Language units
  • Structural units
  • System constraints
  • Role structure of units
View Answer
  • True
  • False
View Answer
  • Greedy
  • Top Down
  • Procedural
  • Step by Step
View Answer
  • Bagging
  • Blending
  • Boosting
  • Stacking
View Answer
  • Poor Data Quality
  • Lack of skilled resources
  • Inadequate Infrastructure
  • None of the above
View Answer
  • Critic
  • Generalizer
  • Performance system
  • All of these
View Answer
  • Platt Calibration
  • Isotonic Regression
  • Both Platt Calibration & Isotonic Regression
  • None of the above
View Answer
  • 2
  • 3
  • 4
  • 5
View Answer
  • Solving queries
  • Increasing complexity
  • Decreasing complexity
  • Answering probabilistic query
View Answer