The scikit-learn docs say it is the signed distance of that sample to the hyperplane.

I've taken the sum of the weights and their corresponding coefficient and added the intercept to that sum but this does not return the value given by the decision_function method.

When doing classification tasks how is the signed distance different from taking the linear summation of the weights, feature values, and intercept?

The scikit-learn docs say it is the signed distance of that sample to the hyperplane.

I've taken the sum of the weights and their corresponding coefficient and added the intercept to that sum but this does not return the value given by the decision_function method.

When doing classification tasks how is the signed distance different from taking the linear summation of the weights, feature values, and intercept?

For example, If I have the following feature values, intercept, and weights I would calculate the distance from the point to the hyperplane as...

$x_1 = 3, x_2 = 2$

$w_1 = -0.28, w_2 = -0.20$

$b = -0.68$

$$\theta^T \cdot \textbf{X} + b$$

When sklearn calculates the decision_function it returns

return safe_sparse_dot(X, self.coef_.T,
                               dense_output=True) + self.intercept_ 

Where self is our fitted classifier and X is the example for which we are calculating the decision function.

How can I replicate what decision_function is doing?

The scikit-learn docs say it is the signed distance of that sample to the hyperplane.

I've taken the sum of the weights and their corresponding coefficient and added the intercept to that sum but this does not return the value given by the decision_function method.

How is the signed distance different from taking the linear summation of the weights, feature values, and intercept?

The scikit-learn docs say it is the signed distance of that sample to the hyperplane.

I've taken the sum of the weights and their corresponding coefficient and added the intercept to that sum but this does not return the value given by the decision_function method.

When doing classification tasks how is the signed distance different from taking the linear summation of the weights, feature values, and intercept?