We use multimodal optic fibers as random hardware projectors, transforming an input dataset into a higher dimensional speckled image set. The goal is to demonstrate that using such randomized data for classification by training a single logistic regression layer improves accuracy compared to training on direct raw images. Interestingly, we found that the classification accuracy achieved is higher than that obtained with the standard transmission-matrix model, a widely accepted tool for describing light transmission through disordered devices. We conjecture that this improved performance could be due to the hardware classifier operating in a flatter region of the loss landscape when trained on fiber data, which aligns with the current theory of deep neural networks.