Implement Neural Network for classification using scikit learn

Neural Networks is a machine learning algorithm. They are used for both classification and regression problems. Neural Networks are supervised algorithms, that is, they require labeled data to train.

Learn more about Neural Networks here.

In this shot, we will implement Neural Network for classification, using the scikit-learn toolkit.

We will use the built-in digits dataset from the scikit-learn library and split it into test and train datasets.

The model will be trained on the training data, and we will use the test data to evaluate the model.

Step 1: Importing necessary libraries

We import the dataset from the sklearn library with built-in sample datasets. We will use the train_test_split function and the the accuracy and confusion matrix metrics from the sklearn library to split the data into train and test samples and to evaluate the results, respectively. Then, we will use the already built model for Neural Network from the sklearn library. The imports will look like this:

import numpy as np
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import confusion_matrix, accuracy_score

Step 2: Loading the `dataset`

We will load the dataset from the sklearn library to a local variable. Now, all the data from the library is in the dataset variable. Let’s look at the code given below:

dataset = load_digits()

Step 3: `Train` `test` split

We will split the dataset into train and test using the sklearn library. We will use a 80-20 split, in which 80 percent data will be train data and 20 percent of the data will be test data. Let’s look at the code given below:

x_train, x_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.20, random_state=4)

Step 4: Making the `Neural Network classifier`

We will make the Neural Network classifier, and call it NN. We will use all the default parameters. More details on the parameters can be found on scikit learn.

NN = MLPClassifier()

Step 7: `Evaluating` the model

We will use the accuracy function to get the accuracy of the model and use the confusion matrix function to find the confusion matrix.

accuracy = accuracy_score(y_test,y_pred)*100

confusion_mat = confusion_matrix(y_test,y_pred)

Then, we will multiply accuracy by 100 to scale it out of 100.

# Step 1
# Importing the necessary libraries
import numpy as np
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import confusion_matrix, accuracy_score 
# Step 2
# Loading the dataset
dataset = load_digits()
# Step 3
# Splitting the data into tst and train
# 80 - 20 Split
x_train, x_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.20, random_state=4)
# Step 4
# Making the Neural Network Classifier
NN = MLPClassifier()
# Step 5
# Training the model on the training data and labels
NN.fit(x_train, y_train)
# Step 6
# Testing the model i.e. predicting the labels of the test data.
y_pred = NN.predict(x_test)
# Step 7
# Evaluating the results of the model
accuracy = accuracy_score(y_test,y_pred)*100
confusion_mat = confusion_matrix(y_test,y_pred)
# Step 8
# Printing the Results
print("Accuracy for Neural Network is:",accuracy)
print("Confusion Matrix")
print(confusion_mat)