In the perceptron model, the weight ww vector is perpendicular to the linear decision boundary at all times. True False

Answer:

True.

Explanation:

In the perceptron model, the weights vector $\mathbf{w}$ determines the orientation of the decision boundary. The decision boundary is defined by the equation $\mathbf{w}^T \mathbf{x} + b = 0$, where $\mathbf{x}$ is the input vector and $b$ is the bias term.

1. The weight vector $\mathbf{w}$ is orthogonal (perpendicular) to the decision boundary, meaning it points in the direction where the classification decision changes.
2. As the perceptron learns during the training process, the weights are adjusted, but they always maintain this perpendicular relationship to the decision boundary.

This intrinsic property allows the perceptron to effectively separate the data points into different classes based on their features. Hence, the statement is true.