We train three ML models to independently predict transition temperatures (T1/2 values) of spin crossover (SCO) complexes: random forest 115 (RF) with a full feature set, RF with features from random-forest-ranked recursive feature addition (RF-RFA), and a kernel ridge regression (KRR) model with features from RF-RFA following the protocol from prior studies. In this work, we used 1000 trees to train the RF and RF-RFA models using sklearn 117 v0.24.2 for a 80%/20% train/test split. Additionally, we trained 1000 RF models by changing the random seed and obtained an average of performance metrics for a given train/test split. We pre-process the features using Z-normalization where each feature is normalized to a zero mean and unit variance over the train and test data. Feature selection reduces the dimensionality of the original representation of the data set by eliminating uninformative features, and this process can lead to improved out-of-sample performance of RF-RFA and RF-RFA KRR models. Feature selection in RF-RFA models is carried out by first assessing model performance when each descriptor is randomly permuted 119 , thus yielding an importance score for each descriptor. These descriptors are then provisionally added in order of their importance to the final feature set, which starts with 10 most important features. Following the procedure from prior work, the RF-RFA procedure only permanently includes a descriptor in the final feature set if its inclusion improves model test set performance (coefficient of determination) by at least 1%. If a descriptor is permanently included, the next feature that is considered is provisionally added to an updated version of the final feature set.