Introduction
Machine learning is a field that has grown significantly in recent years, with the introduction of various algorithms and libraries that make it easy to develop models. One of these libraries is Scikit-Learn, a popular Python library for machine learning. In this article, we will explore the MLPClassifier algorithm in Scikit-Learn, its basic architecture, and how it can be used to build neural networks for classification tasks.
What is MLPClassifier?
MLPClassifier is a type of neural network algorithm that can be used for classification tasks. It is based on the multilayer perceptron (MLP) architecture, which is a type of feedforward neural network. MLPClassifier is capable of learning non-linear relationships between inputs and outputs, making it a powerful tool for classification tasks.
Architecture of MLPClassifier
The MLPClassifier algorithm consists of an input layer, one or more hidden layers, and an output layer. The input layer is responsible for receiving the inputs to the model, which are then passed on to the hidden layers. The hidden layers perform computations on the input data, and the output layer generates the final output of the model.
Training MLPClassifier
Training MLPClassifier involves providing it with a set of labeled training data, and then iteratively adjusting the weights of the connections between the neurons in the network to minimize the error between the predicted outputs and the actual outputs. This process is known as backpropagation and is performed using an optimization algorithm such as stochastic gradient descent.
Hyperparameters of MLPClassifier
There are several hyperparameters that can be tuned when using MLPClassifier. These include the number of hidden layers, the number of neurons in each hidden layer, the activation function used by each neuron, the learning rate, and the regularization parameter.
Using MLPClassifier for Classification Tasks
MLPClassifier can be used for a wide range of classification tasks, including image classification, text classification, and sentiment analysis. In this section, we will explore how to use MLPClassifier for the MNIST handwritten digit classification task.
Loading the MNIST dataset
The MNIST dataset consists of 70,000 images of handwritten digits, with each image represented as a 28×28 pixel array. To load the dataset, we can use the following code:
from sklearn.datasets import fetch_openml
X, y = fetch_openml('mnist_784', version=1, return_X_y=True)
Splitting the dataset into training and testing sets
Before training our model, we need to split the dataset into training and testing sets. We can use the train_test_split function from Scikit-Learn to split the dataset into a 60/40 ratio, with 60% of the data used for training and 40% used for testing.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)
Creating and training the MLPClassifier model
To create an MLPClassifier model, we can use the following code:
from sklearn.neural_network import MLPClassifier
mlp = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=10)
mlp.fit(X_train, y_train)
This creates an MLPClassifier model with two hidden layers, the first with 100 neurons and the second with 50 neurons, and trains it on the training data for 10 iterations.
Evaluating the performance of the model
To evaluate the performance of the model, we can use the score method of the MLPClassifier object. This method returns the mean accuracy on the given test data and labels.
score = mlp.score(X_test, y_test)
print("Accuracy:", score)
Additionally, we can also generate a confusion matrix to get a more detailed view of the model’s performance. A confusion matrix shows the number of true positives, true negatives, false positives, and false negatives for each class in the test set. Scikit-Learn provides the confusion_matrix function in the metrics module for this purpose.
from sklearn.metrics import confusion_matrix
y_pred = mlp.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
print("Confusion matrix:")
print(cm)
The confusion matrix can provide useful information on which classes the model is having difficulty with. For example, we may find that the model has a high number of false positives for a particular class, indicating that the model is incorrectly predicting that class too often.
Another useful metric for evaluating classification models is precision and recall. Precision measures the proportion of true positives among all positive predictions, while recall measures the proportion of true positives among all actual positive instances. Scikit-Learn provides the precision_score and recall_score functions in the metrics module for this purpose.
from sklearn.metrics import precision_score, recall_score
precision = precision_score(y_test, y_pred, average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')
print("Precision:", precision)
print("Recall:", recall)
By using these evaluation metrics, we can get a better understanding of the performance of our MLPClassifier model on the test set, and identify areas where the model may need improvement.