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Questions and Answers
What is the general prediction formula for a linear model in regression?
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?
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:
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?
What happens to the prediction of a linear model when using two features?
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What characterizes the difference between different linear models for regression?
What characterizes the difference between different linear models for regression?
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