Classification in R – feed forward neural network in R

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Classification in R – feed forward neural network in R

Classification is a type of supervised machine learning that is used to predict the class or category of a new observation based on the values of its predictors. One popular method of classification is using feed-forward neural networks (FFNNs).

A feed-forward neural network is a type of artificial neural network that consists of layers of interconnected nodes (neurons) that process and transmit information. In a feed-forward neural network, data flows in one direction from input layer to output layer, passing through one or more hidden layers in between. Each layer consists of a set of neurons, which apply mathematical transformations to the data they receive.

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

The process of building a FFNN 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 normalizing the variables.
  2. Define the model: The next step is to define the structure of the model, including the number of layers and neurons in each layer.
  3. Train the model: The model is trained using the prepared data. The model will learn the optimal weights for the neurons in each layer by minimizing the error between the predicted output and the actual output.
  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 FFNN in R, you can improve the accuracy of the predictions. it’s particularly useful when you have large amount of data, complex non-linear relationship and high-dimensional data. The reason for it is that the training process of FFNNs can be computationally expensive, and requires a lot of data and computational resources. It also requires to carefully choose the architecture of the network and the right hyperparameters to avoid overfitting or underfitting. Additionally, interpretability of the model is often a challenge.

 

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: Classification in R – feed forward neural network in R.

Classification in R – feed forward neural network in R

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