Walking Through My DualVision Iris Classifier: SVM and Gaussian Processes

Introduction

In this blog post, we dive into my DualVision Iris Classifier, which employs two distinct yet powerful machine learning methods: Support Vector Machines (SVM) and Gaussian Processes. This analysis not only covers practical implementation using Python but also provides a deeper mathematical insight into how these algorithms function and make predictions.

Setup and Data Preparation

We start by preparing our environment and the Iris dataset. The Iris dataset, a classic in machine learning, consists of 150 observations across three species of the iris flower, but for our analysis, we focus on binary classification using two species and two features for simplicity.

Import Libraries and Load Data

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import GPy

Data Filtering and PCA

After loading the data, we filter it to use only two classes and the first two features (sepal…