Machine learning has been a subject of interest because of its ability to use the information to solve complex problems, including facial recognition, handwriting detection etc. At times, this is done by having varied tests fitted into the machine learning algorithms, such as tests establishing thresholds, formulating a statistical hypothesis, minimizing mean squared errors and so on. Machine learning algorithms have an ability to minimize errors over the course of time and learn from past mistakes.
Given the fact that we also have problems in machine learning such as unstable data, under fitted models, overfitted models and uncertain future resiliency, what should be done? There are some general guidelines and techniques, known as heuristics, that can be written into tests to mitigate the risk of these issues arising. These are given in detail below.
Check Fit by Cross-Validating
In penetration testing terminology, cross-validation is defined as a method to split all your data into two categories- training and validation.
- Training data is utilized to build the learning model of the machine
- Validation data is required to validate that the model does what is expected of it to be done. It also increases our ability to determine underlying errors and detect them as prevailing in the model.
Training is very special to the machine learning world since machine learning algorithms aim to map previous observations to outcomes. The algorithms usually learn from the data collected and hence, without the initial set of data, the algorithms are of no use. In certain cases, swapping training with validation has also rendered an increase in the number of tests. This is done by dividing the data into two; for instance, initially set 1 is used to train and set 2 to validate, and then correspondingly, these are swapped for the second test. Based on the data possessed by the tester, one could divide the data into minor sets and cross-validate accordingly. Once you have enough data, cross-validation can be done by splitting data into an indefinite amount of sets.
Seam Testing to Lessen Unstable Data
Seams can be defined simply as the integration points between parts of the code base. Under legacy coding, at times, a tester is given a string of code where a tester can sufficiently predict what will happen when that code is fed with a set of data points, although not knowing what it does internally to arrive at the result. Machine learning is somewhat akin to legacy coding, though the algorithms are not exactly legacy code. Hence, much like legacy code, machine learning algorithms must be treated like a black box where data flows in and flows out of the algorithm. These two seams can, therefore, be tested by unit testing data inputs and outputs in order to ensure they are valid within the specified set of preferences.
A good example for above would be testing a neural network. In cases where data that is yielded to the neural network stands between 0 and 1 and the outcome is required to be 1, when the data sums to 1, it would mean it is modelling a percentage. For example, if you possess 3 spinners and 2 widgets, the array of data, in this case, would be 3/5 spinners and 2/5 widgets. In this way, seam testing defines interfaces between pieces of code. It is crucial to note that the more complex the data gets, the more important these seam tests are.
Use Precision and Recall to Monitor for Future Shifts
Recall and precision are two ways of scrutinizing the power of machine learning implementation. The recall is generally defined as a ratio of true positives to the summation of true positives and false negatives. On the contrary, precision is a metric used that checks the percentage of true positives. For instance, a recall of 4/9 would mean that one has 4 true positives out of 9, whereas, a precision of 4/7 would mean that out of 7 yielded to the user, 4 were correct.
However, to calculate precision and recall, it is necessary to have user input. This has the effect of closing the learning loop and improving the data over the course of time due to feeding back of information after being misclassified. For instance, one of the most popular streaming websites Netflix displays a star rating that you would predictably give to a movie or a show, based on the watch history. However, in case you disagree with the predicted rating and rate it differently (or otherwise indicate you’re not interested), the data would be fed into Netflix’s machine learning model for future prediction.
Simplicity is a great virtue while modelling data and the simpler solution is usually the better one.
In testing terms, this means that one should not overfit his data since overfitted models generally just memorize the amount of data given to them. In cases where a simpler solution can be identified, you will notice the patterns versus parsing out the erstwhile data.
One of the better proxies for complexity in a traditional machine learning model is the ability to quickly train the model. For instance, when there are two varying approaches to train a machine learning model, one taking three hours and another taking mere 30 minutes, the one taking less time is usually the better, irrespective of other things being unequal. A workable approach, therefore, is to wrap a benchmark across the code to identify if the model is getting slower over the period of time.
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