In this post, you will learn about how to train an SVM Classifier using Scikit Learn or SKLearn implementation with the help of code examples/samples.
Scikit Learn offers different implementations such as the following to train an SVM classifier.
- LIBSVM: LIBSVM is a C/C++ library specialised for SVM. The SVC class is the LIBSVM implementation and can be used to train the SVM classifier (hard/soft margin classifier).
- Native Python implementation: Scikit Learn provides python implementation of SVM classifier in form SGDClassifier which is based on a stochastic gradient algorithm.
LIBSVM SVC Code Example
In this section, the code below makes use of SVC class (from sklearn.svm import SVC) for fitting a model.
import pandas as pd
import numpy as np
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn import datasets
# IRIS Data Set
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Creating training and test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify = y)
# Feature Scaling
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
# Training a SVM classifier using SVC class
svm = SVC(kernel= 'linear', random_state=1, C=0.1)
svm.fit(X_train_std, y_train)
# Mode performance
y_pred = svm.predict(X_test_std)
print('Accuracy: %.3f' % accuracy_score(y_test, y_pred))
SVM Python Implementation Code Example
In this section, you will see the usage of SGDClassifier (Note from sklearn.linear_model import SGDClassifier)which is native python implementation. The code below represents the implementation with default parameters.
from sklearn.linear_model import SGDClassifier
# Instantiate SVM classifier using SGDClassifier
svm = SGDClassifier(loss='hinge')
# Fit the model
svm.fit(X_train_std, y_train)
# Model Performance
y_pred = svm.predict(X_test_std)
print('Accuracy: %.3f' % accuracy_score(y_test, y_pred))
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