The MLREM algorithm is a very sparse feature selection method that removes less relevant descriptors in a context-dependent way and optimizes the balance between the variance (model complexity) and bias (model simplicity). The Laplacian prior method is used to give zero weight to irrelevant descriptors and automatically remove them from the model. This feature selection algorithm is similar to the LASSO technique, and is based on L1 regression. In a general machine learning modeling procedure, the dataset would be divided into a training set (e.g., 80% of dataset) to generate the model and a test set (e.g., 20% of the dataset) to evaluate the predictive power of the model. In this study, our aim was mainly focused on investigating the contribution of fabrication parameters on PCE, VOC, JSC and FF rather than developing a model for property prediction purposes. Therefore, we derived the models by applying 100% (squared correlation coefficient), and the standard error of estimation (SEE). Table shows the statistics of MLREM models for PCE, VOC, JSC, and FF. These results indicate the significant parameters (descriptors) for each property. Figure shows the contribution as well as the effect of descriptors on each property where the parameters with positive and negative sign increase and decrease the values of property, respectively. The contribution effects indicated how certain modifications can in general increase or decrease PCE, VOC, JSC, and FF. For example, in the 2D cations category, the AVA can increase PCE, VOC, JSC, and FF, while PEA and GUA negatively impact all of them. The mixture of BA:PEA and BA:GUA positively impact all of them. The mixture of BA:AVA, AVA:PEA and AVA:GUA had very small impact on all properties. The mixture of PEA:GUA showed a negative effect on all properties. In summary, within the 2D cations category, GUA exhibited the largest negative effect while the mixture of BA:GUA showed the largest (or second largest) positive effect on PCE, VOC, JSC, and FF, as they have the largest negative and largest positive coefficients respectively.