First reason is that XGBoos is an ensemble method that uses many trees to make a decision so it gains power by repeating itself.

SVM is a linear separator, when data is not linearly separable SVM needs a Kernel to project the data into a space where it can separate it, there lies its greatest strength and weakness, by being able to project data into a high dimensional space SVM can find a linear separation for almost any data but at the same time it needs to use a Kernel and we can argue that there’s not a perfect kernel for every dataset.

Comparing XGBoost and Support Vector Machines (SVM) involves considering their characteristics, strengths, and weaknesses in different contexts. Both are powerful machine learning algorithms, but they excel in different scenarios. Here are some points you can highlight when discussing why XGBoost might perform better than SVM in certain situations:

Scalability and Speed:
- XGBoost is known for its scalability and efficiency. It is designed to handle large datasets and can parallelize the training process, making it faster than SVM in scenarios with a vast amount of data.
- SVM, particularly with certain kernels, can be computationally expensive and may not scale well to large datasets.
Handling Non-linear Relationships:
- XGBoost inherently handles non-linear relationships between features and the target variable through the use of decision trees. It can automatically capture complex patterns in the data without the need for feature engineering.
- SVM may struggle with highly non-linear relationships, especially without the appropriate kernel selection or tuning.
Feature Importance:
- XGBoost provides a natural way to estimate feature importance, helping users understand the impact of different features on the model’s predictions.
- SVMs, while powerful, may not provide as straightforward an interpretation of feature importance, especially in complex models.
Handling Missing Data:
- XGBoost has built-in capabilities to handle missing data, filling in missing values during the training process.
- SVMs might require additional preprocessing steps to handle missing data effectively.
Regularization:
- XGBoost includes regularization terms in its objective function, helping prevent overfitting and improving generalization to new data.
- SVMs also have regularization parameters, but the choice of the regularization term may require careful tuning.
Ensemble Learning:
- XGBoost is an ensemble learning method, combining the predictions of multiple weak learners (trees) to create a strong model. This ensemble approach can enhance predictive performance.
- SVMs typically involve a single model, and while there are ensemble techniques for SVM, they are not as commonly used as in XGBoost.

It’s important to note that the choice between XGBoost and SVM depends on the specific characteristics of the dataset, the problem at hand, and the computational resources available. In some cases, SVM might outperform XGBoost, especially with smaller datasets or when interpretability is a crucial factor. The key is to understand the strengths and weaknesses of each algorithm and choose the one that best suits the particular requirements of the task.