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**Introduction**

Gradient boosting machines (GBM) have become a popular machine learning technique for building predictive models in various fields such as finance, healthcare, and marketing. GBM is a powerful ensemble learning method that combines the predictions of multiple weak learners to create a strong predictive model. This thesis focuses on the application of gradient boosting machines for additive models, which are particularly useful for modeling complex non-linear relationships in data.

**Table of Contents**

1.1 Introduction

1.2 Background of study

1.3 Problem Statement

1.4 Objective of study

1.5 Limitation of study

1.6 Scope of study

1.7 Significance of study

1.8 Structure of the Thesis

1.9 Definition of terms

**Chapter 2: Literature Review**

2.1 Introduction to Gradient Boosting Machines

2.2 Additive Models and their Applications

2.3 Comparison of GBM with other Machine Learning Techniques

2.4 Applications of GBM in Various Industries

2.5 Challenges and Limitations of GBM

2.6 Recent Advances in GBM Algorithms

2.7 Interpretability of GBM Models

2.8 Ensemble Methods in Machine Learning

2.9 GBM Tuning Parameters and Hyperparameter Optimization

2.10 Summary of Literature Review

**Chapter 3: System Design and Methodology**

3.1 Research Design and Methodology

3.2 Data Collection and Pre-processing

3.3 Feature Engineering and Selection

3.4 GBM Model Building

3.5 Performance Evaluation Metrics

3.6 Cross-validation Techniques

3.7 Handling Class Imbalance

3.8 Model Interpretability Techniques

**Chapter 4: System Implementation**

4.1 Software and Tools Used

4.2 Data Preparation and Cleaning

4.3 Model Training and Evaluation

4.4 Hyperparameter Tuning

4.5 Feature Importance Analysis

4.6 Model Deployment and Monitoring

4.7 Visualization of Results

4.8 Performance Comparison with Other Algorithms

**Chapter 5: Conclusion and Summary**

5.1 Summary of Findings

5.2 Contributions of the Study

5.3 Implications for Future Research

5.4 Practical Applications and Recommendations

5.5 Limitations of the Study

5.6 Conclusion

**Thesis Overview**

Gradient boosting machines (GBM) have gained popularity in the machine learning community due to their ability to construct complex predictive models by combining the predictions of multiple weak learners. This thesis focuses on the application of gradient boosting machines for additive models, which are particularly useful for capturing non-linear relationships in data. The literature review provides an overview of GBM algorithms, additive models, applications of GBM in various industries, and challenges in using GBM. The system design and methodology chapter outline the research design, data collection, feature engineering, model building, and evaluation techniques used in this study. The system implementation chapter details the software and tools used, data preparation, model training, hyperparameter tuning, and model deployment processes. The conclusion chapter summarizes the findings, contributions, implications for future research, and limitations of the study. This thesis aims to provide insights into the application of gradient boosting machines for additive models and contribute to the existing body of knowledge in machine learning.

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