Decision Trees are a reliable mechanism to classify data and predict solutions. Splicing in a Decision Tree requires precision; one slight mistake can compromise the Decision Tree’s integrity. Splicing in a Decision Tree occurs using recursive partitioning. Splitting data starts with making subsets of data through the attributes assigned to it. The data is split recursively in repetition until the spliced data at each node is deemed obsolete in predicting solutions. The subset can be similar to the value of the target variable as well. Splicing has to be methodical and repetitive for good accuracy.