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Questions and Answers
Which of the following is the correct notation for the number of training examples in linear regression?
Which of the following is the correct notation for the number of training examples in linear regression?
- m (correct)
- h
- x
- y
What is the purpose of supervised learning in machine learning?
What is the purpose of supervised learning in machine learning?
- To optimize a cost function
- To learn from unlabeled data
- To classify data into categories
- To predict real-valued output (correct)
Which term represents the input variable or features in linear regression?
Which term represents the input variable or features in linear regression?
- h
- x's (correct)
- y's
- m
What is the purpose of the cost function in linear regression?
What is the purpose of the cost function in linear regression?
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What is the hypothesis in linear regression?
What is the hypothesis in linear regression?
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Study Notes
Notation in Linear Regression
- The correct notation for the number of training examples in linear regression is m.
Supervised Learning
- The purpose of supervised learning in machine learning is to make predictions on unseen data based on labeled training examples.
Components of Linear Regression
- The input variable or features in linear regression are represented by the term X.
Cost Function
- The purpose of the cost function in linear regression is to measure the difference between the model's predictions and the actual output values.
Hypothesis
- The hypothesis in linear regression is a function that maps input variables to output variables, denoted by h(x), and represents the predicted output values.
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