A weakness identified in the model was the imbalanced dataset due to the training data containing only one failed reaction. To address this, oversampling was implemented. A condensed version of the reaction data was interpreted by a SMOGN (Synthetic Minority Over-Sampling Technique for Regression with Gaussian Noise) algorithm. The SMOGN algorithm can be particularly useful when the values in the interest of predicting are rare or uncommon, such as the failed reactions in this scenario. The algorithm identifies underrepresented reaction runs from the condensed dataframe and returns a new dataframe with oversampling of under-represented data and undersampling of over-represented data. The algorithm performed as expected, with the newly returned dataframe oversampling the only low yielding reaction in the training set, reaction 16. The results for this can be seen in Figure training size increases before decreasing again. The reason for this could be due to the differences between runs, with some being more challenging to predict than others. This is also seen in the results from cross-validation. Figure This suggests that these runs are more challenging to predict. Some runs perform better in the chronological results, despite having a smaller training dataset. This could be due to the runs in the dataset being more similar. This can be observed with run 17 as it is similar to run 16, since both had poor product formation. In a larger dataset, it could be envisaged that this could allow similar runs or reactions to be identified. This could allow the use of tailored models which would seek to place emphasis within the training data on runs identified as similar to the run of interest and this could create models which could give more accurate results. A small-scale example of this can be observed earlier in the accurate results of run 17 in the chronological use-case, which included also failed run 16 in the small volume of training data.