SVM Classifier in sklearn

SVM Classifier sklearn : Implementation in Sklearn

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You may implement SVM classifier sklearn by importing sklearn.svm package in Python. Here just for classification, You may use SVC() class. If you want to perform the Regression task, You may use SVR() class. Inside the SCV () class you configure the various parameter like kernel{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’}. Here if you do not set the default kernel automatically, it will be “rbf” by default.

 

SVM Classifier sklearn Implementation Stepwise –

Here we are using pipeline method to make the implementation to stream align.

1.Importing required packages for SVC –

The First step here is to import all the requirement  libraries for our example.

import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

Here we have imported various packages. For example like, NumPy for data creation. See here we are not using real data to demonstrate the SVM Classifier sklearn. Because this will rather confuse most of us in the place of clarity. So in order to make the SVM classifier simple, we are using a small dummy dataset. After it, we import the make_pipeline package which is for making code readable and portable. Just after it, We import the StandardScaler() package because we need to apply feature scaling as it uses a distance matrix for choosing the best hyperplane. The last one is SVC() which I have explained various times in this article.

2. Dataset Generation in SVC –

Here we will create the Dataset in the form of X and Y NumPy arrays.  There will be for input sample with two features.

X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
y = np.array([1, 1, 2, 2])

3. SVC model creation –

This is the last and the most important step. As I already explained that we are using the pipelines for the sequential steps. But It is up to you only. You may write sequential code for this.

clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
clf.fit(X, y)

Here is the complete output for the integrated steps.

SVM Classifier sklearn Implemenation
SVM Classifier sklearn Implementation

Here we used the clf.predict([[-0.8, -1]]) to see the output from this model.

That’s all, for now, these are the steps for the simple implementation of the SVM Classifier in sklearn. I hope you have understood this tutorial. If you have any query regarding it then you can contact us for more information on it.

Thanks
Data Science Learner

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