Decision Tree Regression vs. Gradient Boosting Regressor Models for the Prediction of Hygroscopic Properties of Borassus Fruit Fiber
Abstract
:Featured Application
Abstract
1. Introduction
1.1. Background
1.2. Challenges
1.3. Literature Review
1.4. Focus of the Study and Statement of Originality
- Theoretical contributions:
- The study introduces BNF as a potential engineering material, expanding the scope of natural fiber research, and it comprehensively studies its hygroscopic properties using Dynamic Vapor Sorption (DVS).
- The creation, application, and comparison of GBR and DTR models for predicting hygroscopic properties based on primary and small-size experimental data.
- Practical contributions:
- Eco-friendly extraction of Borassus fruit fibres and their identification as potential sustainable materials for construction application.
- Creation of a valuable dataset for future research and informative model comparison for selecting appropriate models in similar cases.
- Application of practical tools for BNF characterization and product development.
2. Materials and Methods
2.1. Extraction of Fiber
2.2. Chemical Treatment
2.3. Physical and Morphological Properties
2.4. Hygroscopic Behavior (Sorption–Desorption)
2.5. Machine-Learning Models
2.5.1. Decision Tree Regression (DTR)
- •
- T.build(X_train, y_train)
- ○
- Recursively split nodes:
- ▪
- Find the best feature f using a splitting criterion (e.g., Giniimpurity, information gain)
- ▪
- Split node into child nodes based on f
- ○
- Stop splitting when criteria met
- •
- ŷ = T.predict(x)
- •
- Metrics on X_test
2.5.2. Gradient Boosting Regressor (GBR)
- •
- ŷ0 = initial model prediction (e.g., average target value)
- •
- M = ensemble of weak learners
- •
- For m = 1 to M:
- ○
- Calculate residuals: ri = yi − ŷi−1
- ○
- Train weak learner hm(x) on (X, r)
- ○
- Update ensemble: ŷi = ŷi−1 + α * hm(xi)
- •
- ŷ = Σ[αm * hm(x)]
- •
- Metrics on X_test
2.5.3. Evaluation Metrics
2.5.4. Framework for Experiments
3. Results and Discussions
3.1. Effect of the Alkaline Treatment on the Fibers’ Morphology and Microstructure
3.2. Sorption–Desorption Characteristics
3.3. Comparative Assessment of DTR and GBR during the Forecasting Process
4. Practical and Theoretical Implications with Examples to Enhance Decision-Making
5. Conclusions
- Borassus fruit fibers exhibit hysteresis in moisture absorption and desorption, influenced by their cellular structure.
- Alkaline treatment alters fiber morphology, leading to increased porosity and a more uniform diameter.
- GBR and DTR models accurately predict the hygroscopic properties of both treated and untreated fibers.
- GBR outperforms DTR in terms of model-fit metrics (R2, MSE, RMSE, and MAE)
- Practical Applications: The developed models can be used to optimize the production and application of Borassus fruit-based materials, leading to improved product performance and reduced costs.
- Research Advancement: The findings provide a foundation for further research into the relationship between fiber structure, chemical treatment, and hygroscopic behavior.
- Industry Impact: The study offers a potential alternative to traditional, time-consuming methods for characterizing fiber properties, contributing to the development of more sustainable and cost-effective materials.
6. Possible Directions for Future Studies
- The manual extraction of fibers may introduce inconsistencies in fiber purity and damage the fiber structure.
- Data variability as natural fibers exhibit variable intrinsic properties
- The ML models may not be optimal for all types of fibers and experimental data.
- Expanding the characterization of Borassus fruit fiber to include mechanical properties, thermal properties, and chemical composition.
- Developing scalable and efficient extraction processes for large-scale fiber production.
- Enhancing the predictive capabilities of the models by incorporating more data and exploring other machine-learning techniques.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Reference | Material Type | ML Technique Used | Inputs | Predicted Outputs | Statistical Evaluation Metrics |
---|---|---|---|---|---|
[27] | Brodatz and Salzburg texture image (fiber) | Decision tree (DT), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) | Fibers images (Brodatz and Salzburg) | Texture | Accuracy |
[26] | Thermoplastic Resins | Decision Tree Regression (DTR) and Random Forest Regression (RFR), Multi Linear Regression (MLR) | Polycarbonate resin with different pigments | Fiber’s color | Pearson correlation coefficient (PCC) |
[19] | Epoxy composite reinforced with jute/basalt hybrid | Gradient Boosting (GB), AdaBoost, and XGBoost | Spindle speed, Feed rate, Depth of cut | Surface roughness | Average, and maximum error |
[40] | Bamboo Fiber-Reinforced, Palm Oil-Based Resin Bio-Composites | Decision Tree Regression (DTR) and Random Forest Regression (RFR), Gradient Boosting (GB), CatBoost, and XGBoost | Bamboo Fiber volume fraction, fiber length (L), fiber diameter (D), the ratio of length to diameter, resin failure stress and resin failure strain | Tensile strength | R2, MSE, and MAE |
[41] | Wheat straw-reinforced polypropylene composites | Gaussian Process Regression (GPR) | Fiber content, hold time, molding pressure, hold temperature | Tensile strength and impact toughness | R2, RMSE, and MAE |
[42] | Glass Fiber Polymer matrix composite | Artificial Neural Network (ANN), Response Surface Methodology (RSM) | Slurry Pressure, Impingement Angle, Nozzle Diameter | Erosion rate | R and MSE |
[43] | Red brick dust-filled glass epoxy composite | Artificial Neural Network (ANN) | Impact velocity, impingement angle, erodent size & temperature | Erosion rate | R2, RMSE, and MAE |
[20] | Concrete-filled steel tubular columns | Gradient Boosting (GB) | Section type, load, specimens, violin plot, and Heat-map | Strengths | R2, MSE, RMSE, MAE, and CoV |
[44] | Eco-Friendly Concrete | Support vector machine (SVM), linear regression (LR) | W/C, RAC%, superplasticizer, and age | Compressive strength | R2 and RMSE |
[29] | FRP-reinforced concrete members | Bayesian optimization algorithm (BOA), support vector regression (SVR) | Epsilon, box constraint, and kernel scale and kernel function | Shear capacity | R2, RMSE, MAE, and FB |
[45] | Sustainable geopolymer composite | Artificial Neural Network (ANN) | RHA substitution proportion, NaOH concentration, and fiber content | Compressive and flexural strength | R2 and RMSE |
Time (min) | Mass (mg) | Relative Humidity RH (%) | Temp. (°C) | Specific Mass Mt (%) | Time (min) | Mass (mg) | Relative Humidity RH (%) | Temp. (°C) | Specific Mass Mt (%) |
---|---|---|---|---|---|---|---|---|---|
7.78 | 10.01 | 24.26 | 2.68 | 0.01 | … | … | … | … | … |
7.97 | 10.3 | 24.26 | 2.69 | 0.06 | … | … | … | … | … |
8.05 | 10.4 | 24.26 | 2.69 | 0.08 | … | … | … | … | … |
8.17 | 10.5 | 24.26 | 2.69 | 0.11 | … | … | … | … | … |
187.74 | 10.66 | 24.27 | 2.75 | 2.28 | 1310.67 | 80.00 | 24.21 | 3.94 | 46.79 |
187.76 | 11.01 | 24.27 | 2.75 | 2.28 | 1310.69 | 80.00 | 24.21 | 3.94 | 46.79 |
187.78 | 11.33 | 24.27 | 2.75 | 2.28 | 1310.71 | 80.00 | 24.21 | 3.94 | 46.79 |
187.8 | 11.61 | 24.27 | 2.75 | 2.28 | 1310.72 | 80.00 | 24.21 | 3.94 | 46.79 |
187.81 | 11.87 | 24.27 | 2.75 | 2.28 | 1310.74 | 80.00 | 24.21 | 3.94 | 46.79 |
187.83 | 12.09 | 24.27 | 2.75 | 2.28 | 1310.76 | 80.00 | 24.20 | 3.94 | 46.79 |
187.84 | 12.29 | 24.27 | 2.75 | 2.28 | 1278.97 | 80.00 | 24.24 | 3.55 | 32.37 |
187.86 | 12.47 | 24.27 | 2.75 | 2.28 | 1278.99 | 80.02 | 24.24 | 3.55 | 32.42 |
187.88 | 12.66 | 24.27 | 2.75 | 2.28 | 1458.86 | 81.26 | 24.25 | 3.95 | 47.09 |
187.9 | 12.83 | 24.27 | 2.75 | 2.28 | 1459.12 | 82.10 | 24.26 | 3.95 | 47.31 |
187.91 | 12.99 | 24.27 | 2.75 | 2.28 | 1459.44 | 83.00 | 24.28 | 3.97 | 47.78 |
187.93 | 13.16 | 24.27 | 2.75 | 2.28 | 1459.84 | 83.90 | 24.31 | 3.99 | 48.65 |
187.95 | 13.33 | 24.27 | 2.75 | 2.28 | 1461.01 | 84.90 | 24.31 | 4.07 | 51.80 |
187.96 | 13.48 | 24.27 | 2.75 | 2.28 | 1461.22 | 85.00 | 24.31 | 4.09 | 52.41 |
187.98 | 13.65 | 24.27 | 2.75 | 2.28 | 1461.24 | 85.00 | 24.31 | 4.09 | 52.46 |
188 | 13.81 | 24.27 | 2.75 | 2.28 | 1642.64 | 85.00 | 24.26 | 4.93 | 83.64 |
188.01 | 13.97 | 24.27 | 2.75 | 2.28 | 1642.66 | 85.00 | 24.26 | 4.93 | 83.64 |
188.03 | 14.1 | 24.27 | 2.75 | 2.28 | 1642.67 | 85.00 | 24.27 | 4.93 | 83.64 |
188.05 | 14.27 | 24.27 | 2.75 | 2.28 | 1642.69 | 85.00 | 24.27 | 4.93 | 83.64 |
188.06 | 14.42 | 24.27 | 2.75 | 2.28 | 1642.71 | 85.00 | 24.27 | 4.93 | 83.64 |
188.08 | 14.56 | 24.27 | 2.75 | 2.29 | 1642.72 | 85.00 | 24.27 | 4.93 | 83.64 |
188.1 | 14.69 | 24.27 | 2.75 | 2.29 | 1642.74 | 85.00 | 24.27 | 4.93 | 83.64 |
188.11 | 14.85 | 24.27 | 2.75 | 2.29 | 1642.76 | 85.00 | 24.27 | 4.93 | 83.64 |
188.13 | 14.99 | 24.27 | 2.75 | 2.29 | 1642.77 | 85.00 | 24.27 | 4.93 | 83.64 |
188.15 | 15.12 | 24.27 | 2.75 | 2.29 | 1642.79 | 85.00 | 24.27 | 4.93 | 83.64 |
DTR | GBR |
---|---|
Structure: Comprises a tree-like structure where nodes represent decisions based on feature values, and leaves represent the output (predicted values). Functioning:
| Structure: An ensemble of multiple weak learners (usually shallow decision trees) combined to form a strong predictive model. Functioning:
|
Evaluation Metrics and Their Equations | Parameter’s Definition | |
---|---|---|
(1) | Where n = represents the total number of data values y i,exp = represents the actual value yi,pred = represents predicted value | |
(2) | n = represents the total number of data values yi,exp = represents the actual value yi,pred = represents predicted value | |
(3) | RMSE= | n = represents the total number of data values yi,exp = represents the actual value yi,pred = represents predicted value |
(4) | n = represents the total number of data values yi,exp = represents the actual value yi,pred = represents predicted value |
Practical Implications | Theoretical Implications |
---|---|
DTR | DTR |
Easy interpretability | Prone to overfitting |
Handling numerical and categorical features without extensive preprocessing | Inaccurate for complex relationships |
Quick development of the model and its training | Inadequate for continuous features |
Best for categorical features | |
Can identify the most important features | |
Wide range applications | |
GBR | GBR |
Easy interpretability | Requires hyperparameter tuning |
High accuracy on complex datasets | Reduce variance |
Flexibility in handling various data types and non-linear relationship | Ensemble of multiple trees thus hard to understand the internal working |
Less prone to overfitting | Accurate for complex nonlinear relationships |
Examples of how the models can enhance decision-making | |
The rationale behind the model’s forecasts can be comprehended during the application of the BNF thanks to the decision rules of the tree’s simplicity. | |
Both models can be used to predict accurately the engineering properties of BNF based on moisture absorption, mass, temperature, etc. by allowing an understanding of the most important factors | |
Models can be used to identify factors to alternate the BNF characteristics to enable improve their properties based on their application | |
Models can be used to determine the BNF failure characteristics for early prevention or reinforcement | |
Models can be used to produce BNF based on specific applications |
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Mahamat, A.A.; Boukar, M.M.; Leklou, N.; Celino, A.; Obianyo, I.I.; Bih, N.L.; Stanislas, T.T.; Savastanos, H., Jr. Decision Tree Regression vs. Gradient Boosting Regressor Models for the Prediction of Hygroscopic Properties of Borassus Fruit Fiber. Appl. Sci. 2024, 14, 7540. https://doi.org/10.3390/app14177540
Mahamat AA, Boukar MM, Leklou N, Celino A, Obianyo II, Bih NL, Stanislas TT, Savastanos H Jr. Decision Tree Regression vs. Gradient Boosting Regressor Models for the Prediction of Hygroscopic Properties of Borassus Fruit Fiber. Applied Sciences. 2024; 14(17):7540. https://doi.org/10.3390/app14177540
Chicago/Turabian StyleMahamat, Assia Aboubakar, Moussa Mahamat Boukar, Nordine Leklou, Amandine Celino, Ifeyinwa Ijeoma Obianyo, Numfor Linda Bih, Tido Tiwa Stanislas, and Holmer Savastanos, Jr. 2024. "Decision Tree Regression vs. Gradient Boosting Regressor Models for the Prediction of Hygroscopic Properties of Borassus Fruit Fiber" Applied Sciences 14, no. 17: 7540. https://doi.org/10.3390/app14177540