Data Generation

The data generation process

We know our model already. To generate synthetic data for it, we need to pick values for its parameters. In our case, we chose b = 1 and w = 2 (as in, thousands of $).

First, let us generate our feature (x), we use Numpy’s rand method to randomly generate 100 (N) points between 0 and 1.

Then, we plug our feature (x) and our parameters b and w into our equation to compute our labels (y). But we need to add some Gaussian noise (epsilon) as well. Otherwise, our synthetic dataset would be a perfectly straight line. We can generate noise using Numpy’s randn method, which draws samples from a normal distribution (of mean 0 and ...