In this post, you learn about Sklearn LibSVM implementation used for training an SVM classifier, with code example. Here is a great guide for learning SVM classification, especially, for beginners in the field of data science/machine learning.
LIBSVM is a library for Support Vector Machines (SVM) which provides an implementation for the following:
- C-SVC (Support Vector Classification)
- epsilon-SVR (Support Vector Regression)
- Distribution estimation (one-class SVM)
In this post, you will see code examples in relation to C-SVC, and nu-SVC LIBSVM implementations. I will follow up with code examples for SVR and distribution estimation in future posts. Here are the links to their SKLearn pages for C-SVC and nu-SVC
Sklearn LibSVM (C-SVC) Code Example
In this section, you will see the code example for training an SVM classifier based on C-SVC implementation within LibSVM. Note that C is a regularization parameter that is used to train a soft-margin classifier allowing for bias-variance tradeoff based on the value of C. A detailed post on C value can be found in this post, SVM as soft margin classifier and C value. Here is the code. Note the instantiation of SVC class in this statement, svm = SVC(kernel= ‘linear’, random_state=1, C=0.1). Iris data set is used for training the 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))
Sklearn LibSVM (Nu-SVC) Code Example
In this section, you will see a code sample on how to train a SVM classifier using nuSVC implementation.
from sklearn.svm import NuSVC # Instantiate the nuSVC implementation nusvc = NuSVC(nu=0.03) # Fit the model nusvc.fit(X_train_std, y_train) # Mode performance y_pred = nusvc.predict(X_test_std) print('Accuracy: %.3f' % accuracy_score(y_test, y_pred))
One could find further details on SVM LIBSVM implementation on this page for building a classifier (classification model).
- Standard Deviation of Population & Sample – Python - August 3, 2020
- Machine Learning – Feature Selection vs Feature Extraction - August 2, 2020
- Sklearn SelectFromModel for Feature Importance - August 2, 2020