Dummy

class sklego.dummy.RandomRegressor(strategy='uniform', random_state=None)

Bases: sklearn.base.BaseEstimator, sklearn.base.RegressorMixin

A RandomRegressor makes random predictions only based on the “y” value that is seen. The goal is that such a regressor can be used for benchmarking. It should be easily beatable.

Parameters

• strategy (str) – how we want to select random values, can be “uniform” or “normal”

• seed (int) – the seed value, default: 42

fit(X: numpy.array, y: numpy.array) → sklego.dummy.RandomRegressor

Fit the model using X, y as training data.

Parameters

• X – array-like, shape=(n_columns, n_samples,) training data.

• y – array-like, shape=(n_samples,) training data.

Returns

Returns an instance of self.

predict(X)

Predict new data by making random guesses.

Parameters

X – array-like, shape=(n_columns, n_samples,) training data.

Returns

array, shape=(n_samples,) the predicted data