Software engineering

from sklearn.neural_network import MLPClassifier model = MLPClassifier() model.fit(X, Y)
Regression
from sklearn.neural_network import MLPRegressor model = MLPRegressor() model.fit(X, Y)
Hyperparameters
As we saw in Chapter 3, ANN has many hyperparameters. Some of the hyperparameters that are present in the sklearn implementation of ANN and can be tweaked while performing the grid search are:
Hidden Layers (hidden_layer_sizes in sklearn)
It represents the number of layers and nodes in the ANN architecture. In sklearn implementation of ANN, the ith element represents the number of neurons in the ith hidden layer. A sample value for grid search in the sklearn implementation can be [(20,), (50,), (20, 20), (20, 30, 20)].
Activation Function (activation in sklearn)
It represents the activation function of a hidden layer. Some of the activation functions defined in Chapter 3, such as sigmoid, relu, or tanh, can be used.
Deep neural network
ANNs with more than a single hidden layer are often called deep networks. We prefer using the library Keras to implement such networks, given the flexibility of the library. The detailed implementation of a deep neural network in Keras was shown in Chapter 3. Similar to MLPClassifier and MLPRegressor in sklearn for classification and regression, Keras has modules called KerasClassifier and KerasRegressor that can be used for creating classification and regression models with deep network.
A popular problem in finance is time series prediction, which is predicting the next value of a time series based on a historical overview. Some of the deep neural networks, such as recurrent neural network (RNN), can be directly used for time series prediction. The details of this approach are provided in Chapter 5.
Advantages and disadvantages
The main advantage of an ANN is that it captures the nonlinear relationship between the variables quite well. ANN can more easily learn rich representations and is good with a large number of input features with a large dataset. ANN is flexible in how it can be used. This is evident from its use across a wide variety of areas in machine learning and AI, including reinforcement learning and NLP, as discussed in Chapter 3.
The main disadvantage of ANN is the interpretability of the model, which is a drawback that often cannot be ignored and is sometimes the determining factor when choosing a model. ANN is not good with small datasets and requires a lot of tweaking and guesswork. Choosing the right topology/algorithms to solve a problem is difficult. Also, ANN is computationally expensive and can take a lot of time to train.
Using ANNs for supervised learning in finance
If a simple model such as linear or logistic regression perfectly fits your problem, don’t bother with ANN. However, if you are modeling a complex dataset and feel a need for better prediction power, give ANN a try. ANN is one of the most flexible models in adapting itself to the shape of the data, and using it for supervised learning problems can be an interesting and valuable exercise.
Model Performance
In the previous section, we discussed grid search as a way to find the right hyperparameter to achieve better performance. In this section, we will expand on that process by discussing the key components of evaluating the model performance, which are overfitting, cross validation, and evaluation metrics.