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

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

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. The number of classifier models depends on the classification technique we are applying to. •One vs. All:- N-class instances then N binary classifier models •One vs. One:- N-class instances then N* (N-1)/2 binary classifier models •The Confusion matrix is easy to derive but complex to understand.

1.define dataset with centers=3 2.summarize dataset shape 3.ummarize observations by class label 4.summarize first few examples 5.plot the dataset and color the by class label

/*
Program to implement the multi class classifier.
Developed by: Gowri M
RegisterNumber: 212220230019
*/

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()

)

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