Using dummy estimators to compare results

This recipe is about creating fake estimators; this isn't the pretty or exciting stuff, but it is worthwhile having a reference point for the model you'll eventually build.

Getting ready

In this recipe, we'll perform the following tasks:

1. Create some random data.
2. Fit the various dummy estimators.

We'll perform these two steps for regression data and classification data.

How to do it...

1. First, we'll create the random data:

from sklearn.datasets import make_regression, make_classification

X, y = make_regression()
from sklearn import dummy
dumdum = dummy.DummyRegressor()
dumdum.fit(X, y)

DummyRegressor(constant=None, quantile=None, strategy='mean')

2. By default, the estimator will predict by just taking the mean of the values and outputting it multiple times::

dumdum.predict(X)[:5]

>array([-25.0450033, -25.0450033, -25.0450033, -25.0450033, -25.0450033])

There are other two other strategies we can try. We can predict a supplied constant (refer to constant=None...