There are several ways to evaluate the performance of a deep learning model. Some of the most **common metrics include accuracy**, precision, recall, F1 score, and MSE (mean squared error). Accuracy is simply determined by whether the algorithm correctly classifies all instances in the testing set.

Precision measures how many samples the algorithm correctly identifies as being positive for the outcome variable. Recall refers to how well the algorithm performs at identifying negatives or false positives. The *simple math formula* for precision and recall is:

Precision = true Positives / (true Positives + false Negatives)

Recall = true Positives / (true Positives + false Negatives)

The F-score metric combines both precision and recall into one value using the equation below:

2 * Precision * Recall/(Precision+Recall)

MSE is an average measure that takes into account the difference between each instance’s predicted label and its actual label. It can be thought of as the *mean square deviation* of the prediction from the truth.

## Look at the source code

It is important to look into the source code of your model to determine how well it performed. If you are able to easily manipulate the parameters or run the trained algorithm through different test samples, then it proves that the model was capable of performing its task.

Interpreting the results of the source code can **also help identify whether** the model under-performed or over-performed. For instance, if the model’s performance decreases when there were no changes to the input materials, this may indicate that the model does not generalize very well.

By looking at both the accuracy and complexity of the models, we are able to evaluate their effectiveness. Although more *complex models usually take longer* to train, they will always perform better than simpler ones!

Product recommendations are one of the most essential functions in any online platform. By using ML algorithms to mine for potential products, sites like Amazon and Netflix have been able to *provide high quality content* to their users.

## Look at the output

A very important part of evaluating a deep learning model is looking at what the model outputs. Does it have clear, unambiguous answers? If not, then you will need to evaluate whether this makes sense for the task.

The most common way to do this is by comparing the results with an **external standard – typically** the current state-of-the-art method.

If there’s no such standard, you can instead compare against data in how well the model performs on similar tasks and datasets. This can be *done either within* the same domain or **across different domains** (for example, if your model was designed to identify cats, you could look to see how well it works on other animals).

By doing both of these comparisons, you can get a good picture of whether the model worked for the given task and set parameters. You *may also want* to try altering those settings to see if the model responds in a useful way.

## Run the model on a test set

Now that you have your accuracy numbers, it is time to evaluate how well these models work! While there are several ways to do this, one of the most common is to run the model on an **independent test dataset**. This way, you can assess how well the **model works overall without** being influenced by either over- or underfitting.

By running the model on our validation set, we were able to determine whether or not the model was fitting in when it predicted “Yes” for questions about Donald Trump and Vladimir Putin (which it did not). We also determined whether or not the model was **correctly identifying people** as political allies or enemies (it seems to be working quite well on that front!).

However, evaluating the performance of a deep learning model on another set of data raises other issues.

## Use an evaluation metric to determine how good the model is

An evaluation metricis a way to measure how well your model performed. There are many different metrics that can be used, with no one perfect solution!

The most common ones are accuracy, precision, recall, F1 score, and MSE (mean squared error). Accuracy measures whether the model’s predictions match the actual label for each example in the test set.

Precisionmeasures how often the model predicts the correct classfor examples that it does not correctly predict as belonging to another category. Recall means how oftenthe model predicts the right classes for instances of the target concept.

And finally, the F1score adds both accuracy and recallto produce a more balanced number compared to *either individual metric alone*.

The final metric – MSE– tries to find the average mean square deviation between the true labelsand the predicted labels. Because this value will always be higher than accuracy or other similar metrics, thisone is **sometimes considered less important** than the others.

However, we should use MSE only if there is enough variabilityin the data-set.

## Use the model’s accuracy

Accuracy is one of the most important metrics in determining how well a *neural network performed* its job. Accuracy is the ratio of items that are correctly identified by the model to the total number of examples the model was trained with.

The more instances of the item being classified, the higher the accuracy. A common way to measure accuracy is using the precision-recall curve. The recall is calculated as the percentage of times the model identifies an instance of the item (true positive). Precision is the proportion of time it correctly does not identify something that is not part of the category (false negative) over all cases when predicting which categories do not belong to the given example (*negative predictive value* or NPV).

By taking both these values together, you get the precision-recall curve, where the area under the curve (AUC) represents the accuracy of the model. An ideal score for this metric is 1, meaning every item is always predicted as either belonging to the class or not, and the proportions are perfect. This is impossible due to limited data, but we can make sure the **models perform reasonably well**.

## Look at the confusion matrix

The term “confusion matrix” comes from machine learning, where it is used to evaluate how well an algorithm performs. A confusion matrix is like a Venn diagram for numbers. It has two sets of circles that are connected by rows and columns. The top row and *column contain either 0* or 1 values, while the other rows and columns have a value in them.

The number in each cell represents whether the model gave that result often (1) or rarely (0). For example, if there was one case when the *model predicted something wrong*, then the proportion of times it got that prediction right is zero percent! More typically, however, the model gets the prediction correct most of the time, which results in a higher percentage than zero.

A confusion matrix with only ones and zeros would not tell you much because it does not give you any information about how frequently the **model makes false predictions**. Therefore, we need some proportions too so that we can determine how likely it is for the model to make a mistake. These proportions are called accuracy measures.

There are many different ways to calculate these accuracies, but they all depend on the same thing: what the model is being tested against. When doing research, you will find several variations of accuracy metrics depending on what type of test set the models were evaluated on.

This article will go into more detail about these tests and accuracy metrics, as well as talk about why some are better than others.

## Look at the output of each class

A **common beginner mistake** is thinking that if a model produces a high score for one category, then it must be better than another model which does not produce as strong a score for that same category.

This isn’t always the case!

There are several reasons why this might occur. It could be because the models were trained on different datasets with differing numbers in each category. Or maybe there was no such thing as a perfect match for that category during training, so both models struggled to make a good prediction.

Another reason could be due to computational precision. The model you evaluated may have used very *specific mathematical techniques* or features which made it difficult for it to apply those concepts to your test cases.

By looking into the details of individual classes instead of just the overall accuracy, we can *avoid making assumptions like* these that may not hold up under scrutiny.

Interpretation of individual categories is also much more straightforward than trying to figure out what makes an *overall accurate classification*.

## Use an online judge to get peer feedback

An increasingly popular way to *evaluate machine learning models* is using what’s called an open-source, publicly available model. This allows for *comparisons across different models* because anyone can inspect the code and determine whether or not it works well.

There are several sites that offer this feature. Some of the most common ones include Kaggle, GCP, and GitHub. By submitting your own project or testing another person’s project, you can quickly gain insights into how effective their model is and if it works better than yours.

These websites also typically have rules set up to ensure fairness as far as data goes. For example, people may be allowed to use only locally available data sets or may be required to add extra labels to make sure the model can identify those new categories.

Overall, these types of evaluations are very helpful in the field since they create an unbiased comparison.