This is perhaps the best-known example in the field of machine learning.
The aim is to classify iris flowers among three species (Setosa, Versicolor, or Virginica) from sepals' and petals' length and width measurements.
The iris data set contains fifty instances of each of the three species.
The central goal here is to design a model that makes useful classifications for new flowers. In other words, one which exhibits good generalization.
This example is solved with Neural Designer. To follow it step by step, you can use the free trial.
This is a classification project, since the variable to be predicted is categorical (setosa, versicolor, or virginica).
The goal here is to model the probabilities of class membership, conditioned on the flower features.
The first step is to prepare the data set. This is the source of information for the classification problem. For that, we need to configure the following concepts:
The data source is the file iris_flowers.csv. It contains the data for this example in comma-separated values (CSV) format. The number of columns is 5, and the number of rows is 150.
The variables are:
Note that neural networks work with numbers. In this regard, the categorical variable "class" is transformed into three numerical variables as follows:
The instances are divided into training, selection, and testing subsets. They represent 60% (90), 20% (30), and 20% (30) of the original instances, respectively, and are randomly split.
We can calculate the distributions of all variables. The next figure is the pie chart for the iris flower class.
As we can see, the target is well-distributed. Indeed, there is the same number of Virginica, Setosa, and Versicolor samples.
The second step is to choose a neural network. For classification problems, it is usually composed by:
The neural network must have four inputs since the data set has four input variables (sepal length, sepal width, petal length, and petal width).
The scaling layer normalizes the input values. As all input variables have normal distributions, we use the mean and standard deviation scaling method.
Here we use 2 perceptron layers:
The probabilistic layer allows to interpret the outputs as probabilities. That is, all outputs are between 0 and 1, and their sum is 1. The softmax probabilistic method is used here.
The neural network has three outputs since the target variable contains three classes (Setosa, Versicolor, and Virginica).
The next figure is a graphical representation of this classification neural network:
The fourth step is to set the training strategy, which is composed of:
The loss index chosen for this application is the normalized squared error with L2 regularization.
The error term fits the neural network to the training instances of the data set. The regularization term makes the model more stable and improves generalization.
The optimization algorithm searches for the neural network parameters which minimize the loss index. The quasi-Newton method is chosen here.
The following chart shows how the training and selection errors decrease with the epochs during training. The final values are training error = 0.005 NSE (blue), and selection error = 0.195 NSE (orange).
The objective of model selection is to find the network architecture with the best generalization properties. That is, that which minimizes the error on the selected instances of the data set.
More specifically, we want to find a neural network with a selection error of less than 0.195 NSE, which is the value that we have achieved so far.
Order selection algorithms train several network architectures with a different number of neurons and select that with the smallest selection error.
The incremental order method starts with a small number of neurons and increases the complexity at each iteration. The following chart shows the training error (blue) and the selection error (orange) as a function of the number of neurons.
As we can see, the order that yields the minimum selection error is two. Therefore, we select the neural network with two neurons in the first perceptron layer.
The purpose of the testing analysis is to validate the generalization performance of the model. Here we compare the neural network outputs to the corresponding targets in the testing instances of the data set.
In the confusion matrix, the rows represent the targets (or real values) and the columns the corresponding outputs (or predictive values). The diagonal cells show the correctly classified samples. The off-diagonal cells show the misclassified samples.
|Predicted setosa||Predicted versicolor||Predicted virginica|
|Real setosa||10 (33.3%)||0||0|
|Real versicolor||0||11 (36.7%)||0|
|Real virginica||0||1 (3.33%)||8 (26.7%)|
Here we can see that all testing instances are well classified, but one. In particular, the neural network has classified one flower as Virginica being Versicolor. Note that the confusion matrix depends on the particular testing instances that we have.
Next, the classification tests are listed. These values are directly calculated from the confusion matrix.
The neural network is now ready to predict outputs for inputs that it has never seen. This process is called model deployment.
To classify a given iris flower, we calculate the neural network outputs from the lengths and withs of its sepals and petals. For instance:
For this particular case, the neural network would classify that flower as being of the virginica species since it has the highest probability.
The mathematical expression of the trained neural network is listed below.
Watch the step by step video tutorial of this example solved with Neural Designer.