Algorithm Evaluation and Parameter Optimization#


This guide is highly opinionated and provides a slightly different view on many guides that primarily focus on the evaluation of machine learning algorithms. With this guide we are trying to generate an overarching understanding on how to approach evaluation for any type of algorithm (ML or not). Is this guide perfect? - Of course not, but we battle-tested it with a bunch of (grad)students in our lab and it seems to be one of the best ways to explain the more complicated aspects of algorithm evaluation (like nested hyper-parameter search). If you think the guide could be improved, or you disagree with certain statements made, let us know in the GitHub issues so that we can improve the guide together.

Whenever we want to apply an algorithm in a new scenario (e.g. different measurement environments, different patient cohorts, different movement patterns, …), it is important to perform an evaluation study during which ground truth information is captured (e.g. motion capture, manual labeling, …). Based on this ground truth data we can estimate the performance of our algorithms on future unlabeled data. Further, we can use such a labeled dataset to optimize parameters of our algorithms. This could be training a machine-learning model or just optimizing a threshold. However, when doing so, we need to make sure that we follow correct procedure to not introduce biases during this optimization step, which could lead to an overly optimistic performance prospect and poor generalization on future data. In the following we will explain how to perform such parameter optimization and evaluation correctly using the example of gait analysis algorithms. In particular, we will discuss when and how to use cross-validation.

The concept of evaluation and labeled data (and the related naming) is heavily influenced by machine learning. However, the concepts discussed here apply, or rather need to be applied, for all “non-machine-learning” algorithms as well. In the following we will provide guides for specific “groups” of algorithms, depending on whether they contain data-driven learning or not.

Categories of Algorithms#

In the following we will introduce 3 different categories of algorithms, we will need to treat differently. These categories differentiate between algorithms with and without trainable/optimizable components, with and without hyperparameter, and with and without regular parameter.


We consider all algorithms trainable that have components that can be directly learned from (labeled) data. This includes all algorithms that we would consider machine learning (e.g. a decision tree) or that use data directly, for example to create a template for event detection. We will call the output of such a training step a model. We specifically do not describe algorithms as trainable that just have parameters (like a decision threshold) that can only be optimized in a brute-force way (e.g. via grid search).


Most algorithms that are trainable also have hyper-parameters. These are parameters that govern or influence the training aspect of the algorithm. This means a change in hyper-parameters will result in a different trained model from the same data. Usually only brute-force/black-box methods exist to find the optimal parameter set. Note, that parameters related to steps like pre-processing of the data can also be considered hyper-parameters. Adjusting them changes the training data and hence, the model.


All remaining variable parts of a model (i.e. values that can not be trained, or do not influence the training) are simply called parameters. Like hyper-parameters, we assume that only brute-force/black-box methods exist to optimize these values.

With these definitions three distinct groups of algorithms emerge:

Group 1

Traditional algorithms without any data-trained aspects fall into this group. Hence, this group has just parameters. All “traditional” algorithm fall into this category. For example, in the context of biosignal analysis, the Pan-Tompkins algorithm to detect R-peaks in a ECG signal [1] or the algorithm by Rammpp et al. to detect heel strikes in a IMU data of walking [2]. Such algorithms, have a couple of parameters (like filter coefficients or the size of search windows), that can either be kept at there “default” values provided in literature or could be optimized by using a brute-force method, to improve results for certain scenarios. However, non of the parameters can directly be “trained” in any way.

Note, that we also consider algorithms to be in Group 1, if they contain trainable components, but we are not modifying them in the context of a certain evaluation/parameter optimization. For example, the use of a pretrained ML model is therefore Group 1. In such a case, we would either have no adjustable parameter or only traditional parameters that govern how we apply the pre-trained model or post-process its output.

Group 2

We consider all traditional machine learning algorithms to belong to this group. These algorithms only have hyper-parameters and a trainable model, but no normal parameters. Thanks to the ongoing hype for machine learning you can find various resources online that explain how to evaluate and optimize such algorithms (for example the sklearn documentation). Therefore, we will only briefly discuss this group in the following.

Group 3

This group consists of all algorithms that are trainable (and have hyper-parameters), but have additional parameters that can not be learned and that do not influence the model. These are often more complex algorithms/pipelines that consist of multiple steps. Fore example a pipeline that uses a ML model to finds events in a time-series followed by a heuristic algorithm that extracts some additional information from each event. If you want to evaluate the combination as a single whole you have the trainable component of the ML model and the traditional parameters of the heursitc part of the pipeline you need to optimize. Another example would be template based (e.g. DTW based or cross-correlation based) algorithms. The generation of the template from existing data can be considered as a data-driven training. The peak/candidate detection to find the actual matching candidates (in the cross correlation or DTW cost function) is usually done by simple thresholding methods only governed by a set of simple parameters. These parameters are not considered hyper-parameters, as they do not influence the template (the result of the trainable part). In result, we have a Group 3 situation where we need to deal with hyper-parameters and normal parameters at the same time. It is important to note, that parameters modifying the pre-processing that we would apply to the data before template generation/ML training should usually be considered hyper-parameters, as they change the template/model. We will see later, why this is an important distinction.

Evaluating an Algorithm (Part 1)#


If you don’t want to optimize parameters or train your algorithm, you don’t need this guide. Just apply your algorithm to your data and report the result. However, as soon as you start to make changes to your algorithms, because you are not happy with the performance you saw, you are optimizing parameters, and should make sure that you follow the procedures explained here.

Independent of which group your algorithm belongs to, you must never evaluate its performance on the same data you trained/ optimized parameters on. Otherwise, we have a train-test leak that results in a far too optimistic evaluation of your algorithm and the performance of the algorithm in the “real world” (aka on unlabeled data in the future) will be much worse than you initially assumed. This means we need to split our labeled data into a train set that we can freely use for training and parameter optimization and a test set, which we never touch except when we want to perform the final evaluation of our algorithm. In other words, the only data we and our algorithms see during development and training is the train data. We must treat our test data as if it doesn’t even exist.

This means when we discuss parameter optimization in the next section, we will only work on the train data. We will come back to our test set in Evaluating an Algorithm (Part 2). In this second part, we will also discuss more sophisticated methods than a simple train-test split. But for now we will assume we just sectioned of a small part of our labeled data as a test set and take the remaining train set into the parameter optimization.

What are we evaluating?#

This might seem like a stupid question, but it is important to understand that in the context of algorithms that have parts that can be trained or optimized, we do not evaluate a specific instance of an algorithm (algorithm + model + parameter set), but rather our entire approach (algorithm + training method + parameter optimization method). Yes, the final performance parameter, that we get when applying our optimized algorithm to the test data, tells us how good a specific instance of our algorithm is. However, because we trained and optimized this instance using our overall approach, it also tells us how good we can expect another instance of our algorithm to be when it is trained with the same approach on different (but similar) data.

Algorithm Optimization#

As mentioned in the previous chapter, algorithm optimization and training must only be performed on the train data in the context of the evaluation. However, it is important to understand that algorithm optimization/training also exists outside the context of evaluation. To facilitate this idea, we will just use the term “data” and not “train-set” in this section to refer to all the data “we are allowed to use”.


In the context of evaluation, the term “data” in this chapter should be read as “train set”. And if the text tells you to use all your data, I mean that you should use all the data you want to use for training and optimization. If you are currently performing an algorithm evaluation, this means your train data. Don’t touch your test data!

What are we optimizing?#

Like with evaluation, we need to understand what we are actually optimizing. For evaluation we concluded that we evaluated an approach that contains the algorithm and the methods used for algorithm optimization. This chapter now describes the potential approaches for algorithm optimization methods. So as the name suggests, we are actually optimizing the algorithm or rather its adjustable components. These can be weights describing a machine learning model or neural network, parameters like thresholds or cutoffs that are used in other parts of the algorithm, and hyper-parameters that change how our weights are adjusted based on our data during training. We want to set all of them to values that lead to the best possible performance on our test data or our future data in production. However, as we don’t have access to our test data or labeled future data during our optimization, we need to choose the values to provide the best performance on the data we have available and then hope that this also results in good performance on our unseen data. This usually requires some measures to prevent overfitting.

With that in mind let’s discuss how we would optimize the weights and parameters of the algorithms in our different groups.

Group 2 - Simple Machine Learning#

We start with Group 2 as this might be the group you are already most familiar with and which has the most amount of information available in the internet, lectures, and books.

Without Hyper-Parameter Optimization#

For traditional ML-models we need to differentiate cases with hyper-parameter optimization from cases without. We will start with the easier one: Any machine learning algorithm must be trained. Aka, the weights that make up its internal model need to be set to values that fit your data. Each algorithm has a different approach on how this should be done most efficiently. Luckily, tools like scikit-learn, and also trainable algorithms in gaitmap, provide sufficient abstractions, so that you just need to call the correct method with your training data as parameter.

With Hyper-Parameter Optimization#

In basically all cases it is advisable to adjust available hyper-parameters of your model. They influence the training and control, for example, how well your model will be able to fit to your data or prevent your model from overfitting. However, as no intrinsic method exists to optimize these parameters (in most cases), we usually have to try different value combinations and see how well they perform. Instead of doing that by hand - or as someone on twitter called it: “graduate student decent” -, we usually use brute-force methods (grid search, random search, …) to test out various parameter combinations. In any case, we need to train a model with each of the parameter combinations, then calculate a performance value for each of them and select the best one.

This raises the question, which data should we use to calculate these performance values? We can not use the test set in the context of evaluation. As we established, this is complete off-limits. However, we can also not use the same data we used for training. Otherwise, we would promote models that heavily overfit. The solution is to split off an additional part of the data that we can use for performance evaluation during hyper-parameter optimization. We call this the validation set to avoid confusion with the test set.

With that we can use the following workflow (represented as pseudo code):

# Optimize hyper-parameter
# We are using the term `inner_train_...` here to avoid confusion with the train set we established during
# evaluation. However, the procedure remains the same, even if we are not in the context of an evaluation.
# Note: "..._gt" is the ground truth information in the respective part of the data.
inner_train_data, inner_train_gt, validation_data, validation_gt = split_validation_set(data, ground_truth)

best_parameter = None
best_performance = 0

for parameter in parameter_space:
    model = algorithm(parameter).train(inner_train_data, inner_train_gt)
    performance = evaluate(model.predict(validation_data), validation_gt)
    if performance > best_performance:
        best_parameter = parameter
        best_performance = performance

# Retrain model with best parameters on all data
final_model = algorithm(best_parameter).train(data, ground_truth)

Note, that after we optimized the hyper-parameters, we didn’t just take the best available model, but just the best hyper-parameters and then retrained the model on all the data we had available during optimization (aka all train data if we perform a evaluation). This ensures that our model can make use of as much data as possible.

This is actually a critical point. In many situations, we don’t have sufficient data available to create a validation set without risking that our hyper-parameter optimization will heavily depend on which data ends up in the validation set. As this split is usually performed randomly, we do not want to take the chance that our entire model fails, because of a bad random split. The solution for that (and actually the recommended way in general) is to use a cross-validation. This allows us to use all (train) data during the hyper parameter optimization by creating multiple validation splits and averaging over all of them.:

# Optimize hyper-parameter
best_parameter = None
best_performance = 0

for parameter in parameter_space:
    performance_over_folds = []
    for fold in range(n_cv_folds):
        inner_train_data, inner_train_gt, validation_data, validation_gt = get_cv_fold(data, ground_truth, fold)
        model = algorithm(parameter).train(inner_train_data, inner_train_gt)
        performance = evaluate(model.predict(validation_data), validation_gt)

    mean_performance_over_folds = mean(performance_over_folds)
    if  mean_performance_over_folds > best_performance:
        best_parameter = parameter
        best_performance = mean_performance_over_folds

# Retrain model with best parameters on all data
final_model = algorithm(best_parameter).train(data, ground_truth)

Note, that we perform the exact same series of data-splits for each parameter combination and then calculate the average performance over all folds for each parameter combination. The combination with the best average performance can then be used to retrain our model.


When using Grid Search, we need to pick a metric we use for evaluation. This depends on your very specific application. But typical candidates are “accuracy”, “F1-score”, or the “Youden-index”. We can also calculate a combination of multiple values, but we need to have a way to decide on the best overall result.

For further explanation and ways to implement that easily, see the sklearn guide

Group 1 - Simple Algorithms#

Algorithms that don’t have any components that would be considered trainable require brute-force methods for optimization. This means we just try out all the different parameter combinations we want to have and pick the best one. However, the same question as before arises: Which data should I use to get these performance parameters?

The (maybe surprising) answer is: All my data! We do not need to provide a separate validation set, if we do not have a training step.

Let’s understand why with two explanation approaches:

Let’s look at our brute-force method as a black-box that provides an optimal set of parameters given some “training” data. If we now simply replace the word “parameters” with “weights” we actually described the concept of a machine learning algorithm. A poor one - yes. But this means we can actually treat our simple algorithm analogous to a machine learning algorithm without hyper-parameters. And as we learned in the previous section for such algorithms, we (simply) try to minimize the loss on all available data and hope that the solution generalizes well to unseen data.

A different way to understand, why we shouldn’t use a validation set to perform a brute force optimization of a simple algorithms, is to just try it and see what happens: So let’s consider the pseudo code for the simple validation split introduced above. Because we don’t have a train step, we directly call predict on the algorithm with the given parameter set. Effectively, we just ignored the inner training set entirely. This means, splitting off a validation set, is equivalent to simply throwing away data. If we would perform a cross-validation, we wouldn’t throw away any data, but in each fold, we would again ignore the training data. This means that the cross-validation would be equivalent to performing the grid search on batches of the data and averaging at the end. In case of a leave-one-out cross-validation (only a single sample/subject is in the train set each fold) this would lead to the exact same result as a Grid Search on all of our data and otherwise the result would be slightly different (because the mean of batch-means is not equal to the mean over all data), but not better in any way.

So, no validation set it is! And we can just perform a simple grid search:

# Optimize parameters
best_parameter = None
best_performance = 0

for parameter in parameter_space:
    performance = evaluate(algorithm(parameter).predict(data), ground_truth)
    if performance > best_performance:
        best_parameter = parameter
        best_performance = performance

final_algorithm = algorithm(best_parameter)

But doesn’t that lead to over-fitting? Maybe… But, with a brute-force approach we can not do anything about it as we have no ways to optimize hyper-parameters or apply regularisation to conquer over-fitting, like we could for Group 2 algorithms. The only thing we can do is to evaluate our approach thoroughly and see if the results are sufficient for our application.

Group 3 - The Hybrid#

The remaining group describes complicated algorithms that are basically hybrids that have both trainable components and parameters that do not effect training, but the final performance. These systems can further have hyper-parameters. However, we will see that this does not change our approach to parameter optimization. But to build an understanding of the approach, let’s start with an algorithm for which we do not want to optimize hyper-parameters.

Without Hyper-Parameter Optimization (only Parameter Optimization)#

There are (or at least there were for me) two approaches that seem plausible at first glance: First, we could consider the parameter optimization as part of the training. This means, in the train step of our algorithm, we first train the actual model component and then, using the predicted outcome on the same data, we use a simple grid search to optimize the remaining parameters. The second approach would be to ignore that our parameters do not effect training and treat them identical to hyper-parameters. This means, we would apply the same strategies we applied to trainable algorithms with hyper-parameter optimization.

Let’s analyze these approaches: In the first case, it seems plausible that we can separate the training of the model component and the optimization of remaining parameters. The model is fix and we can not accidentally make the model more prone to over- or underfit by selecting parameters. However, in this approach we might overfit the parameter optimization. Consider the following example: We want to use a template matching method to find matches in a time series (e.g. for stride segmentation) Hence, we first “train” a new template based on our data and matches provided by a ground truth. Then we want to optimize the concrete detection of matches by optimizing the similarity threshold at which we consider something a match.

In the first approach, we would create our template and then try out different similarity threshold on the same data we already trained the template on. Likely, the match between the template and the data will be pretty good, and hence, the required matching threshold will likely be small. In result our optimization selects a relatively small threshold. However, on our real data (or test data) the template matches less well. Therefore, a higher threshold would be required to find all strides. Aka, our selected algorithm parameters could not generalize well to unseen data, because we overfitted the threshold optimization. This means, the first approach is not suitable, unless we are sure that our model will perform equally well on the train and the test data.

However, the second approach appears to be working. If we treat normal parameters as hyper-parameters and take out a separate validation set, we will train our template on the inner-train set and then test different values of the similarity threshold on the validation set. This way, we perform the parameter optimization based on the model output on unseen data, which should resemble real-world performance.

We could actually reuse the pseudo code shown above (for both the validation set and the cross-validation version), however, this would result in multiple useless calls to train method of our algorithm. As our parameters do not effect the training, it is sufficient to call train only once per CV fold and then only perform the grid search on the validation data. A elegant solution to this, could be to cache the calls to train internally in the model to avoid repeated calculations. To gain further performance, the part of the predict method that just belongs to the model could also be cached.

With Hyper-Parameter Optimization#

If we now reintroduce hyper-parameter, we can stick to the same approach. However, we have to repeat the training for all hyper-parameter combinations. All combinations of the regular parameters can then again be tested based on a cached model.

Group 3 is kind of strange …#

When reading this, you might ask yourselves, which algorithms (besides template matching), could actually be considered Group 3. The special requirement is that it needs to be possible to train the trainable components of the algorithm, even though we only have ground truth available for the final output. But there are a couple of algorithms that fulfil this requirement! In general, all algorithms where the trainable part of the algorithm doesn’t get us all the way, and post processing steps are required to calculate the final output, can be considered Group 3. For example, this could be stride detection algorithms that only provide stride candidates that then are selected based on further criteria. If these criteria have adjustable parameters, we have a Group3 situation.. This could be machine learning algorithms for trajectory estimation that need smoothing of the final output. In both cases, we can train just the machine learning part on the provided training data. But we never expect the algorithm to perfectly reproduce that ideal output. Hence, post processing steps are required that can for various reasons not directly be implemented in the model itself.

But, in the grand scheme of things, it is right to say that few algorithms fall in into Group 3, as many do not fulfill the requirement stated above. For most complicated and chained algorithms, we need to produce ground truth for the intermediate outputs so that parts of the algorithm can be trained/optimized independently. But even in these cases, we should follow the optimization concept outlined above. If we first train our model based on the intermediate ground truth and then optimize the parameters of the remaining steps independently, but on the same data pool, we still risk over-fitting these parameters as explained above. Simply, we expect the output of our model to be better on our training data than on unseen data. This means we optimize our remaining parameters based on this “best case” output (btw. the same would be true if we use the intermediate ground truth as input for the parameter optimization). Depending on the exact model and algorithm, this might not generalize well to unseen data, where the output of the model component is less ideal. Therefore, it would be safer to tune these parameters based on the prediction on unseen data, as shown above in the cross-validation approach.

Evaluating an Algorithm (Part 2)#

Now that we have learned about optimization and training of the algorithm we can get back to evaluating our algorithms and optimization approaches. Our general approach we introduced so far is as follows:

  1. Split the data into train and test data

  2. Perform parameter optimization and algorithm training on the train data

  3. Apply the optimized algorithm instance to the test data and calculate performance parameters.

Note that this is a general approach and Step 2 can be substituted with any of the optimization approaches we learned about above.

In general this way of evaluating is totally fine, but, like with the validation set before, if we do not have enough data, our reported performance might depend on the random split we performed in step 1. In return, our approach could actually be better or worse on real world data. Therefore, to get a more robust (but in general slightly pessimistic) performance estimate, we can repeat the evaluation multiple times with different train-test splits. Aka, we perform a cross-validation. This simply means, we repeat our evaluation workflow in a loop while changing out the train and test split in step 1 in each iteration. As final performance we will report the mean over all cross-validation folds.


When using a cross-validation to evaluate a trainable algorithm with additional (hyper-)parameters, we basically perform a cross-validation within a cross-validation. This is often called a nested cross-validation. I think, this is a bad term to describe the process. It makes it seem that we “apply” a nested cross-validation to an algorithm. But, what we are actually doing is “Using a cross-validation to evaluate an algorithmic approach that uses cross validation for (hyper-)parameter tuning”. Explaining it that way, it is clearer that one cross-validation is an integral part of our approach (even outside the concept of evaluation) and the other one is added to perform the evaluation.

Group 1 Caching#

To aid understanding the similarities between the algorithm types, we described the Grid Search for Group 1 equivalent to training a Group 2 algorithm. However, if we break open that black-box abstraction we can increase the cross-validation performance for Group 1 algorithms quite dramatically.

Where training Group 2 and Group 3 algorithms requires complicated interplay of all the provided training data, the grid search to optimize a Group 1 algorithm only involves calculating the performance for each training sample (e.g. one gait test or one patient) and then averaging the performance to get the estimate for each parameter combination.

If we perform the training multiple times during cross-validation, we have a large overlap between the trainings data of the folds. This means we need to calculate the performance for the same trainings sample over and over again. Therefore, it can be very helpful to cache the output of the prediction step. This basically speeds up a k-fold cross validation by a factor of k-1.

Alternatively to caching, you could also precalculate the performance of each sample in your dataset for all parameter combinations you want to test. Then you simply average over different parts of the calculated results in the different cross-validation folds.

Putting everything together#

In real life applications, evaluating the performance of an algorithmic approach is not where things end. Usually, our overarching goal is to create an algorithms instance (a “production model”) that can be used on future unseen (and unlabeled) data to serve our application. Evaluation is there to tell us if our approach will be good enough for the application we want to accomplish in the real world.

But how do we generate this final algorithms instance? Remember, that our evaluation actually evaluates our approach and not an individual algorithm instance. This means we can confidently use our approach to optimize and train an algorithm instance on all labeled data we have (train and test data), without performing any final evaluation. This seems a little bit risky, but if our prior evaluation was thorough, we can expect our final algorithmic instance to be at least as good as what we saw during the evaluation process.

Before we go through the final workflow to do that, let’s step back for a second and answer the question, why we would want to do that. During the evaluation we already created multiple algorithmic instances (one per cross-validation fold) that ideally were already very capable. Why shouldn’t we use one of these models in production? The issue with each of these models is that it was trained only on a portion of all available labeled data we have, as we needed to hold back some data for testing in each fold. In general, we assume that more data for training/optimization will always lead to better performance. This means, we would expect each of the algorithmic instances we created during cross-validation to generalize a little bit worse on unseen data than an algorithmic instance that was created with all the labeled data we have available. Because of this, it is usually better to create your final model by repeating your training and optimization on your entire dataset, even though you can not provide any performance parameters for this final model.

So with all of that in mind, our full workflow (from idea to production model) would look like that:

  1. Do some experimenting with data and the algorithm to get a better understanding on potential pitfalls and narrow down parameter ranges for potential brute-force optimization. In an ideal world, you would do that after you put some data away, which could serve as some sort of “ultimate” test set, which would even be free from human biases. But, in reality this is rarely done… To learn more about this dilemma of biasing yourself and consequently over-fitting the hyper-parameters see for example this paper. The authors suggest the term “over-hyping” to refer to any issues related to hyper-parameter over-fitting.

  2. Evaluate your approach using cross-validation. In each fold you run your entire approach including your chosen method for parameter optimization and/or training.

  3. Take the average performance result from your cross-validation and decide if the results are good enough for your application. If yes, continue with Step 4. If not, go back to the drawing board. Again: in an ideal world, you should not reuse your dataset, as you now have seen the performance results on the test sets. If you then start to change/optimize your approach based on this knowledge you risk “over-fitting” because of your human bias. But again, this is often unrealistic in the real world, but you should keep that in mind and take steps to reduce bias when applicable. Further, all work that requires multiple iterations of an approach should be considered explorative and findings (in particular generalization errors) should ideally be confirmed by more focused studies in the future.

  4. Create a production model by taking all your available labeled data. Use the production model for all future unlabeled data. In the real world, it is usually advisable to check if future data points are still “in-distribution” (e.g. from the same patient population, measured in the same context, …). If they are not, you would need to obtain labeled data for the new use case and run through the evaluation again and potentially adapt your production model. Further, it is common to update your production model once further labeled data becomes available. This makes sure that your production model always performs as good as possible. It is also common practice to define certain benchmark datasets that can be used to track your changes in performance over multiple iterations of your approach or your production model. In the first case you would perform the entire described evaluation process on these benchmark datasets. In the latter case, the benchmark dataset is used as “ultimate” test set that is excluded from training, even in the production model. This becomes feasible if you have a lot of data and tracking your performance in production is important.

Summary Table#


Read the entire guide before using this table!

Here is a quick summary of how to implement the full approach for each of our algorithm Groups:

Group 1
  • Grid Search

  • cross-validation were you perform a grid search in each fold and select an optimal parameter set per fold. This is different from `GridSearchCV` in `sklearn`!. Note, that the results for each individual train sample can be cached so that you effectively only have to calculate a “single fold”

Group 2 (without hyper-parameters)
  • Algorithm specific training

  • cross-validation were you apply the algorithm specific training to each fold of train data.

Group 2 (with hyper-parameters)
  • Grid Search with an embedded cross-validation for hyper-parameter optimization. You obtain one performance value per parameter combination and fold. Pick the parameter combination that has the best average performance over all folds. Other metrics to pick the best value might be used as well. This is `GridSearchCV` in `sklearn`.

  • Take the best parameter combination and retrain on all the optimization data with the algorithm specific training method.

  • cross-validation, where you perform the entire optimization and retrain step per fold.

Group 3
  • Identical to Group 2, but with optional caching to speed up the process.

  • Identical to Group 2

For all of these approaches you can retrain/reoptimize on all of your data to generate your production model.

A note on …#

… brute-force methods#

In this guide we used brute-force methods basically synonymously with Grid Search. This is not entirely correct. There exist multiple approaches to “just trying out multiple parameter combinations”. There are random search methods, adaptive grid search methods, and methods like Bayes Optimization, that can be much faster and provide better results than naive Grid Search in certain cases. Often, such methods are suitable substitutes to Grid Search and can be used in the same way.

Further, we sometimes just say that parameters can only be optimized by brute-force methods, because we are lazy and do not want to do the math. If you want the best results for one of your parameters (in particular in the Group 1 methods), think about if it is possible to calculate a gradient over your entire algorithm. Basically, can you find a mathematical formulation for the question “How does my performance parameter change if I make a small change to my parameter?”. If this appears feasible, you can use gradient-based optimization methods to find the optimal parameter values. Tools that can automatically calculate gradients over complicated functions (like jax) can help with that.

Even further, there is an entire field of research on black-box optimization of arbitrary systems. Such approaches try to estimate gradients in the (hyper-)parameter plane and allow for gradient based optimization, even when no gradient can be calculated mathematically.

… cross-validation#

In this guide we used cross-validation whenever we performed an evaluation multiple times, because we feared that a single train-validation/test split might be too unstable. However, we did that in two very different scenarios with two different purposes:

First, we learned to use cross-validation during parameter optimization. In this use-case, cross-validation actively prevents over-fitting of hyper-parameters (not the actual model btw.). By performing the hyper-parameter search on multiple validation-test splits, we ensure that our hyper-parameter set would work well on multiple random subsets and not overfit to the validation data of a single split.

The other context in which we learned about cross-validation is evaluation. In this scenario cross-validation can give us a slightly pessimistic generalisation performance. Note, that it does not actively prevent over-fitting, as we must not modify our model based on the outcome of the evaluation. However, the evaluation can provide us insights in how well our approach will generalize.

To summarize: In the first use-case cross-validation is actually a way to improve our model. It is just a “trick” we can use to prevent over-fitting of the hyper-parameters (“overhyping”). If we are confident that our model would work without it, we could just stick to a single validation-test split. This would just be another approach to model training - maybe not optimal - but still correct. This might be something you want to do, if training is extremely expensive (= high computation time).

If we use cross-validation for evaluation, it is used to give us an estimate of the actual real world performance. A single train-test split will often not represent this performance well (unless our dataset is really large). This means, not performing a cross-validation for evaluation could actually be considered a methodological error and you should be highly skeptical of performance results produced on a single train-test split.

The other important thing to note about cross-validation is that different types of cross-validation exist and that there are other algorithms that could fulfill the same function as cross-validation. Depending on your data and your application, other methods (like repeated random splits) might be better than simple cross-validation. Such methods can be used equivalently to cross-validation in the context of this guide.

… computation time#

Using cross-validation and grid search requires our algorithms to be trained over and over again (sometimes even on the same data). This is expensive and can take a loooooooong time. The reality is that real-life constraints on computational power sometimes prevent us to follow all the “ideal world” guidelines. In particular in the deep learning community where datasets are large and training times are long, cross-validation is often substituted with a single train-test split and - instead of grid search - parameters are often optimized based on experience. While this might be less robust, or even might lead to accidental train-test leaks in the hyper-parameter selection due to human biases, it is better than not being able to do an experiment at all. This should absolutely not incentivize you to do the same, if you are annoyed by your computer needing to work for 5 min, but it should simply show you that this guide assumes an “ideal world”, which you can not always expect.