EX NO:03

DATE: 11.04.2022

MULTI-CLASS-CLASSIFICATION

AIM:

To write a python program to implement the multi class classification algorithm .

EQUIPMENTS REQUIRED:

1. Hardware – PCs
2. Anaconda – Python 3.7 Installation / Moodle-Code Runner / Google Colab

RELATED THEORITICAL CONCEPT:

In multi-class classification, the neural network has the same number of output nodes as the number of classes. Each output node belongs to some class and outputs a score for that class. Class is a category for example Predicting animal class from an animal image is an example of multi-class classification, where each animal can belong to only one category.

ALGORITHM:

1. Import the necessary modules.
2. Frame the dataset using make_blobs.
3. Assign the counter value using the Counter function.
4. Using a for loop, plot the points using scatter function.

PROGRAM:

Program to implement the multi class classifier.
Developed by   : VIJAYARAGAVAN ARR
Register Number : 212220230059

from numpy import where
from collections import Counter
from sklearn.datasets import make_blobs
from matplotlib import pyplot
X,y=make_blobs(n_samples=1000,centers=3,random_state=1)
print(X.shape,y.shape)
counter=Counter(y)
print(counter)
for i in range(10):
    print(X[i],y[i])
for label,_ in counter.items():
    row_ix=where(y==label)[0]
    pyplot.scatter(X[row_ix,0],X[row_ix,1],label=str(label))
pyplot.legend()
pyplot.show()

OUTPUT:

RESULT:

Thus the python program to implement the multi class classification was implemented successfully.