Coding sample for SVM
To implement SVM regression in Python, we can use the scikit-learn library, which is a popular machine learning library that provides various algorithms and tools for machine learning tasks. Here's an example code to implement SVM regression in Python using scikit-learn:
# Import required libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
# Load the diabetes dataset
diabetes = datasets.load_diabetes()
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(diabetes.data, diabetes.target, test_size=0.2, random_state=0)
# Create an SVM regression model with a linear kernel and an epsilon value of 0.1
model = svm.SVR(kernel='linear', epsilon=0.1)
# Fit the model using the training data
model.fit(X_train, y_train)
# Predict the target variable using the testing data
y_pred = model.predict(X_test)
# Calculate the mean squared error on the testing data
mse = mean_squared_error(y_test, y_pred)
print('Mean squared error: %.2f' % mse)
# Plot the predicted vs. actual values
plt.scatter(y_test, y_pred)
plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.title('SVM Regression')
plt.show()
In this code, we first load the diabetes dataset from the scikit-learn library. We then split the data into training and testing sets using the train_test_split function. Next, we create an SVM regression model with a linear kernel and an epsilon value of 0.1 using the SVR class. We fit the model using the training data using the fit method. We then predict the target variable using the testing data using the predict method. Finally, we calculate the mean squared error on the testing data using the mean_squared_error function and plot the predicted vs. actual values using the scatter and plot functions.
Note that there are many other parameters that we can tune in the SVM regression model, such as the choice of kernel function, hyperparameters, and regularization parameter C. In this example, we used a linear kernel and an epsilon value of 0.1 for simplicity. In practice, we need to perform hyperparameter tuning and feature selection to optimize the performance of the SVM regression model.
What are heyper paramaeters in SVM?