Question: In machine learning, what is the term for the difference between a model's predicted output and the actual correct output?
Answer:
The term for the difference between a model's predicted output and the actual correct output is "error."
MCQ: In machine learning, what is the term for the difference between a model's predicted output and the actual correct output?
Explanation:
The term for the difference between a model's predicted output and the actual correct output is "error."
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Related Questions
- 1. Which machine learning algorithm is commonly used for solving classification problems, particularly for binary classification?
- 2. What is the primary challenge associated with overfitting in machine learning?
- 3. What type of machine learning technique is used for making a sequence of decisions to maximize a cumulative reward?
- 4. What is the term for the process of grouping similar data points together based on their characteristics?
- 5. In machine learning, what does the term "bias" refer to?
- 6. Which machine learning algorithm aims to find patterns or relationships in data without the need for explicit supervision?
- 7. What is the primary purpose of principal component analysis (PCA) in machine learning?
- 8. In natural language processing (NLP), what is the term for assigning parts of speech to words in a sentence?
- 9. Which machine learning technique is often used for detecting anomalies or rare events in data?
- 10. What is the primary objective of a support vector machine (SVM) in machine learning?
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