  A transition of quantum walk induced by classical randomness changes the
probability distribution of the walker from a two-peak structure to a
single-peak one when the random parameter exceeds a critical value. We first
establish the generality of the localization by showing its emergence in the
presence of random rotation or translation. The transition point can be located
manually by examining the probability distribution, momentum of inertia, and
inverse participation ratio. As a comparison, we implement three supervised
machine learning methods, the support vector machine (SVM), multi-layer
perceptron neural network, and convolutional neural network with the same data
and show they are able to identify the transition. While the SVM sometimes
underestimate the exponents compared to the manual methods, the two
neural-network methods show more deviation for the case with random translation
due to the fluctuating probability distributions. Our work illustrates
potentials and challenges facing machine learning of physical systems with
mixed quantum and classical probabilities.
