Answer:
The answer is
A. True
Step-by-step explanation:
In linear regression, Linear models make a prediction using a linear function of the input features, with one being
For regression, the general prediction formula for a linear model looks as follows:
ŷ = w[0] * x[0] + w[1] * x[1] + ... + w[p] * x[p] + b
Here, x[0] to x[p] denotes the features (in this example, the number of features is p)
of a single data point, w and b are parameters of the model that are learned, and ŷ is
the prediction the model makes. For a dataset with a single feature, this is
ŷ = w[0] * x[0] + b
which you might remember from high school mathematics as the equation for a line.
Here, w[0] is the slope and b is the y-axis offset. For more features, w contains the
slopes along each feature axis. Alternatively, you can think of the predicted response
as being a weighted sum of the input features, with weights (which can be negative)
given by the entries of w.
