Non-Linear Regression in R – feed forward neural networks in R

Non-Linear Regression in R – feed forward neural networks in R

Non-linear regression is a type of statistical analysis that is used to model relationships between variables that are not linear. In other words, it is used to model relationships where the change in one variable is not directly proportional to the change in another variable. One popular method of non-linear regression is using feed forward neural networks.

A feed forward neural network is a type of machine learning model that is inspired by the structure of the human brain. It is made up of layers of interconnected “neurons” that are connected to one another. These neurons process and transmit information through the network.

In R, there are several packages available for building feed forward neural networks, such as the ‘nnet’ and ‘neuralnet’ packages. These packages provide functions for creating and training neural networks, as well as functions for evaluating the performance of the model.

The process of building a feed forward neural network in R typically involves the following steps:

  1. Prepare the data: The first step is to prepare the data for the model. This may involve cleaning the data, splitting it into training and testing sets, and scaling the variables.
  2. Define the network structure: The next step is to define the structure of the network, including the number of layers and the number of neurons in each layer.
  3. Train the model: The model is trained using the prepared data. The model will adjust the weights of the neurons to minimize the error between the predicted and actual values.
  4. Evaluate the model: The model’s performance is evaluated using various metrics such as accuracy, precision, recall, and F1 score.
  5. Make predictions: Once the model is trained and evaluated, it can be used to make predictions on new data.

By using feed forward neural network in R, you can model non-linear relationship and get accurate predictions. It can also help in dealing with large amount of data and complex relationships between variables.


In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Non-Linear Regression in R – feed forward neural networks in R.

Non-Linear Regression in R – feed forward neural networks in R

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