Machine Learning Tutorial

MLPClassifier stands for Multi-layer Perceptron classifier which in the name itself connects to a Neural Network. Unlike other classification algorithms such as Support Vectors or Naive Bayes Classifier, MLPClassifier relies on an underlying Neural Network to perform the task of classification.

Implementing MLPClassifier With Python

Here some steps by which we can implement MLPClassifier with Python

It contains three layers input, hidden and output layers.

Import all libraries

Here some important libraries which use to implement MLPClassifier in python

# load libraries
    from sklearn import datasets
    from sklearn import metrics
    from sklearn.neural_network import MLPClassifier
    from sklearn.neural_network import MLPRegressor
    from sklearn.model_selection import train_test_split
    import matplotlib.pyplot as plt    
    import seaborn as sns

Importing the Dataset

Here we are using the breast_cancer data from sklearn

# load the iris datasets
    dataset = datasets.load_breast_cancer()
    X = dataset.data; y = dataset.target

Split data sets

Now we will split the data using train_test_split

#split dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

Fit it into the model

Now we are ready to fit it into the model

# fit a CART model to the data
    model = MLPClassifier()
    model.fit(X_train, y_train)
    print(); print(model)

Make Prediction

Now we are predicting the model

# make predictions
    expected_y  = y_test
    predicted_y = model.predict(X_test)

Classification report and confusion matrix

Now, here we will find the result and confusion matrix

# summarize the fit of the model
    print(); print(metrics.classification_report(expected_y, predicted_y))
    print(); print(metrics.confusion_matrix(expected_y, predicted_y))

#machinelearning #python #datascience