Selected MCQ

Current Question
What is 'ensemble learning' in machine learning?
  • A. Training deep networks only
  • B. Combining predictions of multiple models
  • C. Feature scaling process
  • D. A single-model hyperparameter search
Correct Answer: B
Explanation:
Ensemble learning combines predictions from several models to improve overall performance.
Related Question 1
Which algorithm is best suited for classification tasks in machine learning?
  • A. Linear regression
  • B. K-means
  • C. Support vector machine
  • D. PCA
Correct Answer: C
Explanation:
Support Vector Machine is commonly used for classification problems.
Related Question 2
In machine learning, what is 'dropout'?
  • A. Dropping the dataset
  • B. Randomly omitting neurons during training
  • C. Pruning decision trees
  • D. Deleting old data
Correct Answer: B
Explanation:
Dropout is a regularization technique in which random neurons are randomly deactivated during training to prevent overfitting.
Related Question 3
What does "CNN" typically stand for in machine learning?
  • A. Circular Neural Network
  • B. Convolutional Neural Network
  • C. Cascaded Neural Net
  • D. Cluster Network Model
Correct Answer: B
Explanation:
CNN stands for Convolutional Neural Network, often used in image processing tasks.
Related Question 4
What does the term "bagging" refer to in ensemble learning?
  • A. A bag is placed around the dataset
  • B. Training multiple models on different random subsets and aggregating results
  • C. Sequentially training models to correct errors (boosting)
  • D. A feature engineering technique
Correct Answer: B
Explanation:
Bagging (Bootstrap Aggregating) trains multiple models on bootstrapped data samples and averages their predictions to reduce variance.
Related Question 5
What is the purpose of an epoch in machine learning training?
  • A. A type of activation function
  • B. A full pass through the entire training dataset
  • C. A regularization technique
  • D. A specific type of loss function
Correct Answer: B
Explanation:
One epoch means the learning algorithm has processed the entire training dataset once.