Neural Network

Although sklearn focuses on traditional Machine Learning, it still provides some methods to build a simple forward neural network. In this lesson, we give a simple introduction about how to use it.

Modeling with MLPClassifier

Let’s skip data loading and splitting, and create an MLPClassifier object from the neural_network module. The MLP stands for a multilayer perceptron. As you can see, the NN requires a lot of parameters. If you are familiar with Deep Learning, you may know that fine-tuning is a very important and time-consuming task in a neural network.

Following are some parameters we set below:

• batch_size: In general, a neural network uses stochastic optimizers, so every time it uses a mini-batch sample to train.
• solver: Optimizer is another big topic in Deep Learning, here we just choose the simplest one.
• shuffle: Whether to shuffle

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