SVM From Scratch

Tanvi Penumudy
Jan 17 · 2 min read

Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning.

The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane.

Source: Javatpoint

Image Source: R-bloggers

For the conceptual overview of SVM, refer — A Beginner’s Introduction to SVM

We shall now go through the code walkthrough for the implementation of the SVM algorithm from scratch:

import numpy as np 

class SVM:

def __init__(self, learning_rate=0.001, lambda_param=0.01, n_iters=1000): = learning_rate
self.lambda_param = lambda_param
self.n_iters = n_iters
self.w = None
self.b = None

def fit(self, X, y):
n_samples, n_features = X.shape

y_ = np.where(y <= 0, -1, 1)

self.w = np.zeros(n_features)
self.b = 0

for _ in range(self.n_iters):
for idx, x_i in enumerate(X):
condition = y_[idx] * (, self.w) - self.b) >= 1
if condition:
self.w -= * (2 * self.lambda_param * self.w)
self.w -= * (2 * self.lambda_param * self.w -, y_[idx]))
self.b -= * y_[idx]

def predict(self, X):
approx =, self.w) - self.b
return np.sign(approx)
from sklearn import datasetsdef accuracy(y_true, y_pred):
accuracy = np.sum(y_true == y_pred)/len(y_true)
return accuracy
X, y = datasets.make_blobs(n_samples=50, n_features=2, centers=2, cluster_std=1.05, random_state=40)
y = np.where(y == 0, -1, 1)

clf = SVM(), y)
print(clf.w, clf.b)
y_pred = clf.predict(X)
acc = accuracy(y, y_pred)
print("Training Accuracy: ",acc)
Training Accuracy: 1.0

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For complete code implementation:

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