The main strength of ML is the ability to find patterns and relationships that even an experienced researcher may not be able to find. However, the lack of education on the subject and the misinterpretation of the ML nature may threaten the development of advanced and accurate ML models in chemistry. ML algorithms are often referred to as black boxes. However, this is a misinterpretation because a good ML model requires a detailed analysis and adjustment of hyperparameters, which requires the knowledge of how the algorithms and their implementation work. Some chemists also consider ML algorithms as statistical learning or a kind of "advanced chemometrics," which is not valid, and these misconceptions lead to the inadequate application of ML in chemistry. To overcome such issues, initiatives on chemical education and ML have appeared .