Linear Models for Regression
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Questions and Answers

What is the general prediction formula for a linear model in regression?

  • $ŷ = w_1x_1 - w_2x_2 + ... - w_px_p - b$
  • $ŷ = w_1x_1 / w_2x_2 / ... / w_px_p + b$
  • $ŷ = w_1x_1 * w_2x_2 * ... * w_px_p * b$
  • $ŷ = w_1x_1 + w_2x_2 + ... + w_px_p + b$ (correct)

    • In the equation for a line with a single feature, what does 'w' represent?

  • Coefficient
  • Intercept
  • Exponent
  • Slope (correct)

    • For datasets with many features, linear models can be very powerful because:

  • They can only model targets with specific values of y.
  • They cannot handle datasets with multiple features.
  • They are limited to modeling linear relationships between features.
  • They can perfectly model any target y as a linear function if there are more features than training data points. (correct)

    • What happens to the prediction of a linear model when using two features?

    <p>It becomes a plane</p> Signup and view all the answers

    What characterizes the difference between different linear models for regression?

    <p>How the model parameters and complexity are learned from training data</p> Signup and view all the answers

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