Support Vector Machines (SVM) are a powerful class of supervised learning algorithms used for classification and regression tasks. In this post, we will explore the application of SVM and its variants to classify the species of the famous Iris dataset.
Before diving into the implementation of SVM, let's explore the Iris dataset to gain insights into its structure and characteristics.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Load the Iris dataset
iris = sns.load_dataset('iris')
# Display the first few rows of the dataset
print(iris.head())
Output:
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa
The Iris dataset consists of 150 samples, with 50 samples for each of the three species: setosa, versicolor, and virginica. The dataset contains four features: sepal length, sepal width, petal length, and petal width, all measured in centimeters.
Let's visualize the distribution of each feature using a pairplot:
# Visualize the distribution of each feature
sns.pairplot(iris, hue='species')
plt.show()
The pairplot provides a matrix of scatter plots showing the relationships between different features. The diagonal elements of the matrix represent the univariate distribution of each feature using kernel density estimation (KDE).
From the pairplot, we can observe that the setosa species is clearly separable from the other two species based on the petal length and petal width features. However, there is some overlap between the versicolor and virginica species, especially in the sepal length and sepal width features.
To further analyze the dataset, let's calculate some summary statistics: