# Cascade Forest-Based Model for Prediction of RNA Velocity
## 3 Discussion  
This paper introduces a novel machine-learning algorithm cascade forest model for RNA velocity prediction and classification tasks. The cascade forest model is a decision tree ensemble approach that has fewer hyperparameters than deep neural networks and preserves the tree model's interpretability. Its model complexity can be determined automatically in a data-dependent manner, making cascade forest models work well even on small-scale data. This paper aims to effectively predict RNA velocity using cascade forest models to prove that machine learning (ML) algorithms based on ensemble strategies are effective in predicting RNA velocities. In this paper, experiments were performed on four scRNA-seq datasets with different complexities and splicing ratios. The cascade forest model was comprehensively evaluated with five base classifiers: XGBoost, RandomForest, LightGBM, XGBoost, and TabNet. The experimental results show that the kappa coefficient, accuracy, and F1_score performance of the cascade forest model are significantly better than XGBoost, RandomForest, LightGBM, XGBoost, and TabNet, which proves that the model substantially improves RNA velocity prediction and classification.  
In addition, this paper compares the cascade forest model with the stacking model proposed by [PERSON] et al. for RNA velocity prediction and classification. Through parametric analysis, it is found that cascade forests have better stability and stronger robustness. Especially for the change of parameter d, the cascade forest model prediction accuracy will not fluctuate as much as the stacked model, and even the performance will show a significant downward trend. As parameter k increases, the number of features increases, and the cascade forest model can predict RNA velocity more accurately, showing better performance than the stacking model.  
In this study, we extensively analyze RNA velocity prediction classification problems. Although the first application of existing RNA velocity models has shown promising results [33], the selection of characteristic genes and the screening of highly variable genes have been found in experimental tests to have different degrees of influence on speed prediction. Methods for assessing gene selection bias, joint models for better potential  
Figure 5: The effects of hyper-parameter d. (**A–D**) Performance of cascade forest models and stacking models under different datasets and different parameters d.  
spatial representation, and factor models for unraveling compositional effects will be further studied, which will be necessary for future work.  
Most classification tools accurately predict the results in RNA velocity prediction on four datasets with different splicing ratios. However, this result is based on an ensemble learning framework to balance different sample feature ratios to arrive at different baseline models, but it is still an empirical approach. When the data distribution is incomplete and unbalanced, the prediction results still have a more significant impact. In experimental tests on the Bonemarrow dataset, we found that the accuracy of cascade forest models decreased by more than 20% when the data were unbalanced. Therefore, the interpretability of the model will be a problem that needs to be solved in the future.  
In conclusion, cascade forest models provide a new prediction-based method for studying the mechanisms of cell differentiation that can be applied to help attribute state spaces not yet covered by scRNA-seq data. In future work, we can use this interpolated direction information to conduct more in-depth research on trajectory inference. For example, differential geometry is used to extract potential adjustments for estimating the curvature of the differentiation landscape in metabolic labeling experiments [34]. Therefore, the prediction of RNA velocity can more intuitively understand the trend of cell dynamics, which has specific reference significance for biologists' research.