The purpose of testing is to compare the outputs from the neural network against targets in an independent set (the testing instances). Note that the testing methods are subject to the project type (approximation or classification).
If all the testing metrics are considered ok, then the neural network can move to the so-called deployment phase. Note also that the results of testing depend very much on the problem at hand, and some numbers might be right for one application but bad for another.
The validation methods that need to be used depending on the application type:
The most used testing methods in approximation applications are the following:
To test a neural network, you may use any of the errors described in the loss index page as metrics.
The most critical errors for measuring the accuracy of a neural network are:
The errors statistics measure the minimums, maximums, means, and standard deviations of the errors between the neural network and the testing instances in the data set.
The following table contains the basic statistics on the absolute and percentage error data when predicting the electricity generated by a combined cycle power plant.
Error histograms show how the errors from the neural network on the testing instances are distributed. In general, a normal distribution centered at 0 for each output variable is expected here.
The next figure illustrates the histogram of errors made by a neural network when predicting sailing yachts' residuary resistance. Here the number of bins is 10.
It is very useful to see which testing instances provide the maximum errors, and alert for deficiencies in the model.
The following table illustrates the maximal errors of a neural network for modeling the adhesive strength of nanoparticles.
As we can see, particles with a shear rate of 75 seem to yield the most significant errors.
Linear regression analysis is the most standard method to test the performance of a model in approximation applications.
Here the neural network outputs and the corresponding data set targets for the testing instances are plotted.
This analysis leads to 3 parameters for each output variable:
The first two parameters, a and b, correspond to the y-intercept and the slope of the best linear regression relating to scaled outputs and targets. The third parameter, R2, is the correlation coefficient between the scaled outputs and the targets.
For a perfect fit (outputs exactly equal to targets), the slope would be 1, and the y-intercept would be 0. If the correlation coefficient is equal to 1, then there is a perfect correlation between the outputs from the neural network and the targets in the testing subset.
The following figure is a plot of the linear regression analysis for predicting the noise generated by airfoil blades. As we can see, all the predicted values are very similar to the real ones.
The parameters led by this analysis are correlation = 0.952, intercept = 13.9 and slope = 0.89. From that, the correlation coefficient is the most important parameter. As this value is very close to 1, we can say that the neural network predicts the noise very well.
The most common testing methods in classification applications are the following:
In the confusion matrix, the rows represent the target classes in the data set and the columns the corresponding output classes from the neural network
The diagonal cells in each table show the number of cases that were correctly classified, and the off-diagonal cells show the misclassified cases.
For binary classification, positive means identified, and negative means rejected. Therefore, 4 different cases are possible:
Note that the output from the neural network is, in general, a probability. Therefore, the decision threshold determines the classification. The default decision threshold is 0.5. The output above is classified as positive, and an output below is classified as negative.
For the case of two classes, the confusion matrix takes the following form:
|Predicted positive||Predicted negative|
The following example is the confusion matrix for a neural network that assesses the risk of credit card clients' default.
|Predicted positive||Predicted negative|
|Real positive||745 (12.4%)||535 (8.92%)|
|Real negative||893 (14.9%)||3287 (63.8%)|
For multiple classification, the confusion matrix can be represented as follows:
|Predicted class 1||···||Predicted class N|
|Real class 1||#||#||#|
|Real class N||#||#||#|
The following example is the confusion matrix for the classification of iris flowers from sepal and petal dimensions.
|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%)|
As we can see, all testing instances are correctly classified, except one, which is an iris virginica that has been classified as an iris versicolor.
For binary classification, there are a set of standard parameters for testing the performance of a neural network. These parameters are derived from the confusion matrix:
The following lists the binary classification tests corresponding to the confusion matrix for assessing the risk of default of credit card clients that we saw above:
From those parameters, we can see that the model is classifying very well the negatives (specificity = 81.0%), but it has not such a high precision for the positives (sensitivity = 58.2%).
A standard method for testing a neural network in binary classification applications is to plot a ROC (Receiver Operating Characteristic) curve.
The ROC curve plots false positives rate (or 1 - specificity) on the X-axis, and true negatives rate (or sensitivity) on the Y-axis for different values of the decision threshold.
An example of the ROC curve for diagnosing breast cancer from fine-needle aspirate images is the following.
The capacity of discrimination is measured by calculating the area under the curve (AUC). A random classifier would give AUC = 0.5 and a perfect classifier AUC = 1.
For the above example, the area under the curve is AUC = 0.994. That is an excellent value, indicating that the model is predicting almost perfectly.
The cumulative gain analysis is a visual aid that shows the advantage of using a predictive model as opposed to randomness.
It consists of two lines:
The next chart shows the results of the analysis for a model that predicts if a bank client is likely to churn. The blue line represents the positive cumulative gain, the red line represents the negative cumulative gain, and the grey line represents the cumulative gain for a random classifier.
In this case, by using the model, we see that by analyzing the 50% of the clients with the higher probability of churn, we would reach 80% of clients going to leave the bank.
This method provides a visual aid to evaluate a predictive model loss. It consists of a lift curve and a baseline. The lift curve represents the ratio between the positive events using a model and without using it. Baseline represents randomness, i.e., not using a model.
The x-axis displays the percentage of instances studied. The y-axis displays the ratio between the results predicted by the model and the results without the model. Below is depicted the lift chart of a model that predicts if a person is going to donate blood.
To explain the usefulness of this chart, let suppose that we are trying to find the positive instances of a data set. If we try to find them randomly, after studying 50% of the instances, we discover the 50% of the positive instances.
Now, let suppose that we first study those instances that are more likely to be positive according to the score that they have received from the classifier. After studying 50% of them, we find 80% of the positive instances.
Therefore, by using the model, after studying 50% of the instances, we would discover 1.6% more positive instances than by looking for them randomly. This advantage is what is represented in the lift chart.
Positive rates tell us how the selection of positives changes by using a classification model.
In marketing applications, the positive rate is called the conversion rate. Indeed, it depicts the number of clients that respond positively to our campaign out of the number of contacted clients.
The next chart shows two rates. The first column represents the data set rates, while the second one represents the ratios for the predicted positives of the model.
This testing method shows the difference in profits from randomness and those using the model depending on the instance ratio.
In this chart, three lines are represented. The grey one represents the profits from randomly choosing the instances. The blue line is the evolution of the profits when using the model. Finally, the black one separates the benefits from the losses of the process. The chart below represents the profit chart of a model that detects forged banknotes.
The values of the previous plot are displayed below.
The maximum profit is the value of the greatest benefit obtained with the model, 122.7%, and the instance ratio is the percentage of instances used to obtain that benefit, 45%.
When performing a classification problem, it is essential to know which instances have been misclassified. This method shows which actual positive instances are predicted as negative (false negatives), and which actual negative instances are predicted as positive (false positives).
This information is displayed in a table format, identifying each instance with an Instance ID, and showing its corresponding data, which is composed of its values of the model variables. Two tables would be displayed, one for the positive instances predicted as negative, and the other one for the negative instances predicted as positive.
To learn more about testing analysis, you can read the 6 testing methods for binary classification article in our blog.
All testing methods used for approximation models are valid for forecasting models.
But there are specific testing methods for prediction models. Some of them are the following:
A visual method for testing a forecasting model is to plot the time series data and their corresponding predictions against time, to see the differences.
The next chart shows the one step ahead average temperature in a city (blue) and their corresponding predictions (orange).
The error autocorrelation function describes how prediction errors are correlated in time.
The following chart depicts the error autocorrelation for the 1-day temperature forecast in a city. The abscissa represents the lags, and the ordinate their corresponding correlation value.
As we can see, lag 1 correlation is positive (0.344). That means that if our prediction error was big yesterday, we might expect a big prediction error today.
This task calculates the correlation between the inputs and the prediction error.
The following chart depicts the error cross-correlation for the 1-day temperature forecast in a city. The abscissa represents the lags, and the ordinate their corresponding correlation value.
As we can see, although the cross-correlations have small values, they are positive. That means that, in general, the bigger prediction value, the bigger error we can make.