Python

Python

Sample Implementation

Implementing SVM and Kernel SVM with Python's Scikit-Learn
A support vector machine [https://en.wikipedia.org/wiki/Support_vector_machine] (SVM) is a type of supervised machine learning classification algorithm. SVMs were introduced initially in 1960s and were later refined in 1990s. However, it is only now that they are becoming extremely popular, owing to their ability to achieve brilliant results.
Implementing SVM and Kernel SVM with Python's Scikit-Learn
https://stackabuse.com/implementing-svm-and-kernel-svm-with-pythons-scikit-learn/
Implementing SVM and Kernel SVM with Python's Scikit-Learn
 

Example 1

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values

#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X = sc_X.fit_transform(X)
y = sc_y.fit_transform(y)

#Fitting SVR to the dataset
from sklearn.svm import SVR
regressor = SVR(kernel = 'rbf')
regressor.fit(X, y)

#Predicting a new result
y_pred = regressor.predict(6.5)
y_pred = sc_y.inverse_transform(y_pred)

#Visualizing the SVR results
X_grid = np.arange(min(X), max(X), 0.01) #this step required because data is feature scaled.
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color = 'red')
plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')
plt.title('Truth or Bluff (SVR)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()
notion image
 

Major Kernel Functions in Support Vector Machine (SVM)

Gaussian Kernel Radial Basis Function (RBF)
RBF核函数是一种常用的非线性核函数。它将数据映射到无穷维度的高维度空间，并以高斯分布的方式表达。RBF核函数适用于许多非线性问题，但它有许多超参数需要调整，包括带宽参数（gamma）。
from sklearn.svm import SVC
classifier = SVC(kernel ='rbf', random_state = 0)
# training set in x, y axis
classifier.fit(x_train, y_train)
 
Sigmoid Kernel: this function is equivalent to a two-layer, perceptron model of the neural network, which is used as an activation function for artificial neurons.
from sklearn.svm import SVC
classifier = SVC(kernel ='sigmoid')
classifier.fit(x_train, y_train) # training set in x, y axis
 
Polynomial Kernel: It represents the similarity of vectors in the training set of data in a feature space over polynomials of the original variables used in the kernel.
多项式核函数将数据映射到高维度空间，并引入了多项式特征。多项式核函数允许SVM处理一定程度的非线性数据。
from sklearn.svm import SVC
classifier = SVC(kernel ='poly', degree = 4)
classifier.fit(x_train, y_train) # training set in x, y axis
 
Linear Kernel: used when data is linearly separable.
线性核函数是最简单的核函数，它在原始特征空间中执行内积操作，将数据从低维度映射到高维度，但不引入新的特征。它适用于线性可分的数据。
from sklearn.svm import SVC
classifier = SVC(kernel ='linear')
classifier.fit(x_train, y_train) # training set in x, y axis