Question

Use the multi-layer perceptron algorithm to learn a model that classifies IRIS flower dataset.

Split the dataset into a train set to train the algorithm and test set to test the algorithm. Calculate the accuracy.

Use Scikit-Learn

Answer 1

#Python3 code

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

from sklearn.neural_network import MLPClassifier # neural network

from sklearn.model_selection import train_test_split

from sklearn import metrics

from sklearn.datasets import load_iris

iris = load_iris() #load iris dataset

X=iris.data #input variable

y=iris.target #output variable

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.5) #spilting of the data into train and test

clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(3, 3), random_state=1)

clf.fit(X_train,y_train) #training the MLP classifier on the training data to create the model

prediction = clf.predict(X_test) #model validation on test data

print("Predicted Class Label:\n")

print(prediction)

print("Actual Class Label:\n")

print(y_test)

print('The accuracy of the Multi-layer Perceptron is:',metrics.accuracy_score(prediction,y_test)) #calculate the model accuracy.

#OUTPUT

Predicted Class Label:

[2 1 0 0 0 0 2 1 1 2 0 1 2 0 0 1 2 1 0 2 0 0 2 1 0 0 0 2 1 0 1 2 2 2 2 2 1
0 2 2 1 2 1 1 0 0 1 2 0 0 0 1 0 2 2 1 2 2 2 0 2 2 2 2 0 0 1 2 0 0 1 1 2 2
0]
Actual Class Label:

[2 1 0 0 0 0 2 1 1 2 0 1 2 0 0 1 2 1 0 1 0 0 2 1 0 0 0 2 1 0 1 2 2 2 2 2 1
0 2 2 1 2 1 1 0 0 1 2 0 0 0 1 0 2 2 1 1 2 2 0 2 2 1 2 0 0 1 2 0 0 1 1 2 2
0]
The accuracy of the Multi-layer Perceptron is: 0.96