Gaussian processes are not restricted to regression. We can also use them for classification. As we saw in Chapter 4, Generalizing Linear Models, we turn a linear model into a suitable model to classify data by using a Bernoulli likelihood with a logistic inverse link function (and then applying a boundary decision rule to separate classes). We will try to recapitulate model_0 from Chapter 4, Generalizing Linear Models, for the iris dataset, this time using a GP instead of a linear model.
Let's invite the iris dataset to the stage one more time:
iris = pd.read_csv('../data/iris.csv')
iris.head()
| sepal_length | sepal_width | petal_length | petal_width | species | |
|---|---|---|---|---|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |
We are going to begin with the simplest...
