How to use MLP Classifier and Regressor in Python

Multi-Layer Perceptron (MLP) is a type of neural network that is used for supervised machine learning tasks, like classification and regression. It’s known for its ability to learn non-linear relationships in the data. In this article, we will go over the basics of how to use MLP Classifier and Regressor in Python.

First, we need to import the necessary libraries such as Numpy and Pandas, which will help us handle our data. Next, we will import the MLPClassifier or MLPRegressor class from the sklearn.neural_network library, which will be used to create our model.

Once we have our libraries and classes imported, we can start creating our model. To do this, we will first need to load our data into a Pandas dataframe. We can do this by using the read_csv function, which will allow us to read in data from a CSV file.

Once our data is loaded, we will need to split it into training and testing sets. This is important because it allows us to test the accuracy of our model on unseen data. We can do this using the train_test_split function, which will randomly split our data into training and testing sets.

Now that our data is ready, we can create our model. We do this by instantiating the MLPClassifier or MLPRegressor class and then fitting it to our training data using the fit method. Once the model is trained, we can use it to make predictions on our testing data using the predict method.

To check the accuracy of our model, we can use different metrics such as accuracy score, precision, recall, and f1-score for classification and R2 score, mean squared error (MSE) for regression.

Lastly, we need to optimise our model. One way to do this is by tuning the model’s parameters. The most important parameters are the number of hidden layers, the number of neurons in each layer, and the learning rate.

 

In this Machine Learning Recipe, you will learn: How to use MLP Classifier and Regressor in Python.

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