5 Questions
Which of the following is the correct notation for the number of training examples in linear regression?
m
What is the purpose of supervised learning in machine learning?
To predict real-valued output
Which term represents the input variable or features in linear regression?
x's
What is the purpose of the cost function in linear regression?
To measure the accuracy of the hypothesis
What is the hypothesis in linear regression?
h
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.
This quiz tests your knowledge of linear regression with one variable, model representation, and machine learning concepts. You will be presented with a graph and asked to analyze the housing prices in Portland, OR based on the size of the house. This quiz is designed to assess your understanding of supervised learning and regression problems.
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