Machine Learning Multiple Choice Questions (MCQ) Online Test #20

Machine Learning MCQ #191:

What is the primary goal of a "decision tree" algorithm?

Machine Learning MCQ #192:

In which type of machine learning is "unsupervised learning" commonly used?

Machine Learning MCQ #193:

What is "overfitting" in the context of machine learning models?

Machine Learning MCQ #194:

Which metric is commonly used to evaluate the performance of a regression model?

Machine Learning MCQ #195:

What is the purpose of using "gradient descent" in machine learning?

Machine Learning MCQ #196:

Which of the following is a common technique for feature selection?

Machine Learning MCQ #197:

What is the primary difference between "bagging" and "boosting" in ensemble methods?

Bagging increases the training dataset size, while boosting decreases it

Bagging uses linear models, while boosting uses nonlinear models

Bagging combines models in parallel, while boosting combines models sequentially

Bagging improves model accuracy by adding more features, while boosting uses fewer features

Bagging reduces overfitting, while boosting increases it

About Machine Learning Multiple Choice Question (MCQ) #197:
This ICT Multiple Choice Question (ICT MCQ) #197 focuses on Machine Learning within the "Advanced ICT MCQ" category. This question tests the primary difference between "bagging" and "boosting" in ensemble methods.

Machine Learning MCQ #198:

Which type of neural network is best suited for processing sequential data, such as time series or text?

Machine Learning MCQ #199:

What does "hyperparameter tuning" involve in machine learning?

Machine Learning MCQ #200:

What is the purpose of "normalization" in data preprocessing?

This page was last modified on Dec 30, 2024 by ICT MCQ Hub.

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Machine Learning Multiple Choice Questions (MCQ) Online Test #20

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