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How to select the correct test options when assessing machine learning algorithms

The test options you leverage when assessing ML algorithms can mean the difference between over-learning, a mediocre outcome and a usable state-of-the-art outcome that you can confidently vouch for. 

In this blog post by AICorespot, you will find out about the conventional test options you can leverage in your algorithm evaluation test harness and how to select the right options the next time. 


The foundation of the difficulty in selecting the correct test options is randomness. Nearly all machine learning algorithms leverage randomness in some fashion. The randomness might be explicit in the algorithm or might be in the sample of the information chosen to train the algorithm. 

This does not imply that the algorithms generate random outcomes, it means that they generate outcomes with some noise or variance. We refer to this variant of limited variance, stochastic and the algorithms that exploit it, stochastic algorithms. 

Train and Test on Same Data 

If you possess a dataset, you might wish to undertake training of the model on the dataset and report the outcomes of the model on that dataset. That’s how good your model is. 

The problem with this strategy of assessing algorithms is that you indeed will be aware of the performance of the algorithm on the dataset, but do not have any indication of how the algorithm will feature performance on information that the model was not trained on (so-called unseen data) 

This makes a difference, only if you wish to leverage the model to make forecasts on unobserved data. 

Split Test 

A simple method to leverage a single dataset to both train and estimate the performance of the algorithm on unobserved data is to split the dataset. You take the dataset, and split it into a training dataset and a test dataset. For instance, you arbitrarily choose 66% of the examples for training and leverage the remaining 34% as a test dataset. 

The algorithm is run on the training dataset and a model is developed and evaluated on the test dataset and you obtain a performance precision, let’s state 87% classification precision. 

Split tests are quick and great when you possess a lot of data or when training a model is expensive (be it resources or time-intensive). A split test on a really big dataset can generate a precise estimation of the actual performance of the algorithm. 

How good is the algorithm on the data? Can we with confidence state that it can accomplish a precision of 87%. 

A problem is that if we split the training dataset again into a differing 66%/34% split, we would obtain a differing outcome from our algorithm. This is referred to as model variance. 

Multiple Split Tests 

An answer to our problem with the split test obtaining differing outcomes on differing splits of the dataset is to minimize the variance of the random procedure and to do it several times. We can gather the outcomes from a fair number of runs (say 10) and take the average. 

For instance, let’s state we split our dataset 66%/34%, ran the algorithm and obtained a precision and this was done 10 times with 10 differing splits. We might possess 10 precision scores as follows: 87, 87, 88, 89, 88, 86, 88, 87, 88, 87 

The average performance of our model is 87.5%, with a typical deviation of approximately 0.85 

An issue with several split tests is that it is feasible that a few data instances are never included for training or evaluation, where as others may be chosen several times. The effect is that this might skew outcomes and might not provide a meaningful notion of the precision of the algorithm. 

Cross Validation 

An answer to the problem of ensuring every instance is leveraged for training and evaluating an equivalent number of times while minimizing the variance of a precision score is to leverage cross validation. Particularly, k-fold cross validation, where k is the number of splits to make in the dataset. 

For instance, let’s select a value of k=10 (very typical). This will split the dataset into 10 parts (10 folds) and the algorithm will be executed 10 times. Every time the algorithm is executed, it will be trained on 90% of the data and evaluated on 10%, and every run of the algorithm will alter which 10% of the data the algorithm is evaluated on. 

In this instance, every data instance will be leveraged as a training instance precisely 9 times and as a test instance a single time. The precision will not be a mean and a standard deviation, but rather will be an exact precision score of how many right predictions were made. 

The k-fold cross validation strategy is the go-to method for assessing the performance of an algorithm on a dataset. You wish to select k-values that provide you a good sized training and test dataset for your algorithm. Not too disproportionate (too large or small for training or test). If you possess a lot of data, you might have to resort to either sampling the data or reverting to a split test. 

Cross validation does provide an unbiased estimate of the algorithms performance on unobserved information, but what if the algorithm itself leverages randomness. The algorithm would generate different outcomes for the same training data every time it was trained with a differing arbitrary number seed (beginning of the sequence of pseudo-randomness). Cross validation is not accountable for variance in the algorithm’s predictions. 

Another point of concern is that cross validation itself leverages randomness to determine how to split the dataset into k folds. Cross validation does not furnish estimation on how the algorithm will perform with differing groupings of folds. 

This only makes a difference if you wish to comprehend how robust the algorithm is on the dataset. 

Multiple Cross Validation 

A method to account for the variance in the algorithm itself is to execute cross validation several times and take the mean and the standard deviation of the algorithm precision from every run. This will provide you with an estimation of the performance of the algorithm on the dataset and an estimation of how robust (the size of the standard deviation) the performance is. 

If you possess a single mean and standard deviation for algorithm A and another mean and standard deviation for algorithm B and they differ (for instance, algorithm A has a higher precision), how could we know if the difference is meaningful. 

This only makes a difference if you wish to contrast the outcomes between algorithms. 

Statistical Significance 

A solution to contrasting algorithm performance measures when leveraging several runs of k-fold cross validation is to leverage statistical significance tests (such as the Student’s t-test) 

The outcomes from several runs of k-fold cross validation is a listing of numbers. We like to summarize these numbers leveraging the mean and standard deviation. You can perceive of these numbers as sample from an underlying populace. A statistical significance test provides a solution to the question: are two samples drawn from the same populace? (no difference.) If the solution is “yes” then, even if the mean and standard deviations differ, the difference can be stated to be not statistically noteworthy. 

We can leverage statistical significance tests to provide meaning to the differences (or lack of) amongst algorithm outcomes when leveraging several runs (like several runs of k-fold cross validation with differing random number seeds) This can be when we wish to make precise claims about outcomes (algorithm A was better than algorithm B and the difference was considerably significant) 

This is not the conclusion of the story, as there are differing statistical significance tests (parametric and nonparametric) and parameters to those tests (p-value). We are going to wind up here as if you have listened to us thus far, you now possess enough knowledge about choosing test options to generate rigorous outcomes. 


In this blog post, you found out about the difference amongst the primary test options available to you when developing a test harness to assess machine learning algorithms. 

Particularly, you learned the utility and problems with: 

  • Training and evaluation on the same dataset 
  • Split tests 
  • Multiple split tests 
  • Cross validation 
  • Multiple cross validation 
  • Statistical significance  

When unsure, leverage k-fold cross validation (k=10) and leverage multiple runs of k-fold cross validation with statistical significance tests when you wish to meaningfully contrast algorithms on your dataset. 

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