  The domain of online learning has experienced multifaceted expansion owing to
its prevalence in real-life applications. Nonetheless, this progression
operates under the assumption that the input feature space of the streaming
data remains constant. In this survey paper, we address the topic of online
learning in the context of haphazard inputs, explicitly foregoing such an
assumption. We discuss, classify, evaluate, and compare the methodologies that
are adept at modeling haphazard inputs, additionally providing the
corresponding code implementations and their carbon footprint. Moreover, we
classify the datasets related to the field of haphazard inputs and introduce
evaluation metrics specifically designed for datasets exhibiting imbalance. The
code of each methodology can be found at
https://github.com/Rohit102497/HaphazardInputsReview
