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

Take Machine Learning MCQ Quiz & Online Test to Test your Knowledge

We have listed below the best Machine Learning MCQ Questions, that checks your basic knowledge of Machine Learning. This Machine Learning MCQ Test contains 50+ Machine Learning Multiple Choice Questions. You have to select the right answer to every question to check your final preparation. apart from this, You can also download below the Machine Learning MCQ Pdf completely free.

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