SVMs in High-Dimensional Spaces

SVMs are particularly effective in handling high-dimensional data, where the number of features is large compared to the number of samples. For example, maybe you are measuring a rare event but have several powerful instruments to gather hundreds of data points about them once they do occur. We call this a wide dataset. In this recipe, we will look more closely at how SVMs can be applied to high-dimensional data, both synthetically generated and from real-world datasets.

Getting ready

Before applying SVMs to high-dimensional data, let’s ensure we have the necessary Python libraries installed and the dataset loaded:

1. Load the libraries:

   from sklearn.model_selection import train_test_split
   from sklearn.svm import SVC
   from sklearn.datasets import make_classification
   from sklearn.metrics import accuracy_score, classification_report
   import pandas as pd
   import numpy as np

2. Create a synthetic high-dimensional dataset:

   X, y = make_classification(n_samples...