/Neural_Networks

Fit four different neural networks: (a) Two distinct single hidden layer neural networks. (b) Two distinct neural networks with two hidden layers. Compare the accuracy of these four Neural networks among them. Also compare it to other classification methods.

Primary LanguagePython

Neural-Networks

We perform analysis of the sonar data using neural networks. For 100 independent replications (i.e., 100 independent training /testing splits),

  • Fit four different neural networks:

-Two distinct single hidden layer neural networks: (i) A single hidden layer with 15 nodes (ii) A single hidden layer with 18 nodes:

-Two distinct neural networks with two hidden layers: (iii) Two hidden layers with 23 and 20 nodes and (iv) Two hidden layers with 28 and 23 nodes.

  • Compare the accuracy of these four Neural networks among them.

  • Also compare it to other classification methods. Specifically, we will compare neural network performance to the methods below:

    Linear Discriminant Analysis (LDA). Quadratic Discriminant Analysis (QDA). Regularized Discriminant Analysis(RDA).

    Support Vector Machines (SVMs):

    Naïve Bayes.

Data

 Sonar Dataset. To generate in R Code, we use : library(mlbench) and data(Sonar)

Code implementation.

R packages: neuralnet
  • The comparison will be possible by implementing simulated accuracy matrices. We calculate the average accuracy for each method and compare these values.

Conlusion

-Among the four Neural Networks, the one with the best performance is the Neural Network with Two hidden layers with 28 and 23 nodes (NeuralNet_28_23).

-Among all the classification methods The accuracy of the Superlearner method is the best. Followed by RandomForest and ( Neural Network with Two hidden layers with 28 and 23 nodes) NeuralNet_28_23. Then, NeuralNet_18 , NeuralNet_15, Gradient Boosting method etc.