Contents
What are neural network techniques?
Neural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data. They are used in a variety of applications in financial services, from forecasting and marketing research to fraud detection and risk assessment.
How neural networks can be used for regression?
Neural networks are flexible and can be used for both classification and regression. Regression helps in establishing a relationship between a dependent variable and one or more independent variables. Regression models work well only when the regression equation is a good fit for the data.
How do you evaluate a neural network regression model?
1 Answer
- MSE, aka Mean Squared Error: take all the errors, square them, and find the mean.
- RMSE, aka Root Mean Squared Error: Squared root of MSE.
- SSE, aka Sum of Squared Errors: take all the errors, square them, and compute their sum.
Which is an example of a regression based neural network?
For this example, we use a linear activation function within the keras library to create a regression-based neural network. We will use the cars dataset. Essentially, we are trying to predict the value of a potential car sale (i.e. how much a particular person will spend on buying a car) for a customer based on the following attributes:
Which is an example of a parametrised linear regression?
The previous example of linear regression is an example of parametrised model, where a and b are the parameters. Along this document we will mainly deal with this kind of models because we want to show the transition up to neural networks which are (highly) parametrised.
Which is the best API for neural networks?
Keras is an API used for running high-level neural networks. The model runs on top of TensorFlow, and was developed by Google. The main competitor to Keras at this point in time is PyTorch, developed by Facebook.
Can you train a neural network in R?
A neural network is a computational system that creates predictions based on existing data. Let us train and test a neural network using the neuralnet library in R. A neural network consists of: