Bayesian regression is similar to linear regression, as seen in Chapter 3, Multiple Regression in Action, but, instead of predicting a value, it predicts its probability distribution. Let's start with an example: given X, the training observation matrix, and y, the target vector, linear regression creates a model (that is a series of coefficients) that fits the line that has the minimal error with the training points. Then, when a new observation arrives, the model is applied to that point, and a predicted value is outputted. That's the only output from linear regression, and no conclusions can be made as to whether the prediction, for that specific point, is accurate or not. Let's take a very simple example in code: the observed phenomenon has only one feature, and the number of observations is just 10:
In: from sklearn.datasets import make_classification from sklearn.datasets import make_regression X, y = make_regression(n_samples=10, n_features=1, n_informative=1,...