SVM(Support Vector Machines)

Human Understandable

One Sentence Description

SVM is a machine learning algorithm that finds the optimal boundary to separate data points into different classes, maximizing the margin between them, and can handle both linear and nonlinear data.

Key Essence

Discriminative algorithm


Machine Learning –> Supervised Learning


SVM is memory-efficient, robust, versatile, and effective in high-dimensional spaces. It also uses the kernel trick to solve complex classification problems that other algorithms cannot handle.

Core Principle

SVM (Support Vector Machine) works by finding the hyperplane that best separates the data points into different classes using a kernel function. It selects the hyperplane that maximizes the margin between the classes and identifies the closest data points to the hyperplane, called support vectors. It then uses these support vectors to classify new data points.

Detailed Explanation

SVM stands for Support Vector Machine, which is a type of supervised learning algorithm used for classification and regression analysis. It is widely used in machine learning and data mining for pattern recognition and data classification. The main objective of SVM is to identify the hyperplane that maximally separates the two classes in a given dataset. This algorithm works by mapping the input data into a high-dimensional feature space, where the data can be separated by a hyperplane. SVM is a powerful machine learning algorithm that can handle complex datasets with high accuracy.

Human Runnable

from sklearn import datasets
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

# Load dataset
iris = datasets.load_iris()

# Split dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(,, test_size=0.3)

# Create a svm Classifier
clf = svm.SVC(kernel='linear') # Linear Kernel

# Train the model using the training sets, y_train)

# Predict the response for test dataset
y_pred = clf.predict(X_test)

# Model Accuracy
print("Accuracy:", clf.score(X_test, y_test))

# Detailed report of classification
print(classification_report(y_test, y_pred, target_names=iris.target_names))

Human Visible