DNN vs XGBoost: What are the differences?

# Introduction

DNN (Deep Neural Network) and XGBoost (Extreme Gradient Boosting) are two popular machine learning algorithms used for predictive modeling tasks. Below are the key differences between the two algorithms:

1. **Model Complexity**: DNN is a type of artificial neural network that consists of multiple layers to extract features and patterns from data. It is known for its ability to learn complex patterns and relationships in data, making it suitable for tasks with large amounts of data and features. On the other hand, XGBoost is an ensemble method that builds a series of weak learners in a sequential manner, each correcting the errors of its predecessor. It is generally less complex than DNNs but excels in handling structured/tabular data.

2. **Training Speed**: DNNs are often computationally intensive to train, especially when dealing with large datasets and architectures with many layers. Training a DNN can take longer compared to XGBoost due to the iterative nature of gradient descent optimization. In contrast, XGBoost is known for its fast training speed, as it optimizes the model by adding new weak learners that focus on the residual errors of the previous models.

3. **Interpretability**: DNNs are often viewed as "black box" models, meaning it can be challenging to interpret and explain their predictions due to the complex interactions between neurons in the network. In contrast, XGBoost provides more interpretability as it creates an additive model that can be easily visualized. Feature importance can also be extracted from XGBoost models, allowing users to understand the impact of different variables on the predictions.

4. **Handling Missing Values**: DNNs require data preprocessing techniques such as imputation to handle missing values in the dataset, as neural networks cannot inherently deal with missing data. XGBoost, on the other hand, can handle missing values internally during the training process, making it more convenient for datasets with missing data without the need for imputation.

5. **Regularization Techniques**: DNNs can be prone to overfitting due to their complexity, requiring regularization techniques such as dropout and L2 regularization to prevent overfitting. XGBoost, on the other hand, has built-in regularization techniques such as learning rate shrinkage and tree pruning, making it less susceptible to overfitting without the need for additional regularization.

6. **Parallel Processing**: XGBoost is designed for parallel processing, allowing for faster training on multicore CPUs and distributed environments. In contrast, DNNs are typically trained on GPUs to take advantage of their parallel processing capabilities, which may require additional hardware resources for efficient training.

In Summary, DNNs are suitable for handling complex data patterns with large datasets, while XGBoost excels in speed, interpretability, and handling structured/tabular data efficiently.