How can I extract individual class probabilities from the probability VectorUDT column when using a Spark Random Forest model to predict customer churn?

[Ragita]

To fulfill the aforementioned requirement, we can utilize a “Split Probability” node. This node is employed after the “Spark Predict” node and takes the probability column in array format as an input DataFrame. It performs the task of splitting the vectorudt into two separate columns. The first column, labelled as “prob0,” captures the probability of a customer not churning (i.e., 0), while the second column, labelled as “prob1,” captures the probability of a customer churning (i.e., 1). This enables an efficient segregation of the probability values for further analysis or decision-making.