Introducing Feature Crosses

Can a feature cross truly enable a model to fit nonlinear data? To find out, try this exercise.

Task: Try to create a model that separates the blue dots from the orange dots by manually changing the weights of the following three input features:

• x₁
• x₂
• x₁ x₂ (a feature cross)

To manually change a weight:

1. Click on a line that connects FEATURES to OUTPUT. An input form will appear.
2. Type a floating-point value into that input form.
3. Press Enter.

Note that the interface for this exercise does not contain a Step button. That's because this exercise does not iteratively train a model. Rather, you will manually enter the "final" weights for the model.

(Answers appear just below the exercise.)

---

---

Click the plus icon for the answer.

• w₁ = 0
• w₂ = 0
• x₁ x₂ = 1 (or any positive value)

If you enter a negative value for the feature cross, the model will separate the blue dots from the orange dots but the predictions will be completely wrong. That is, the model will predict orange for the blue dots, and blue for the orange dots.