Five Steps to Debug an ML Model 

Machine Learning (ML) models commonly encounter bugs during the development process. The issues include bad training data, lack of convergence rate, or a high loss error. As developers debug the issues manually, it is hard to know why the errors occur.

To debug the ML model, developers must follow the model development best practices. Keeping the model simple with minimal features helps track the performance gain of each feature.

Furthermore, iteratively optimize the model by adding features, more layers and nodes, and tuning hyper-parameters to achieve an acceptable model accuracy. 

Tracking all the hyper-parameter value ranges is essential to avoid re-trying on the non-workable hyper-parameters, saving time and effort. Lastly, checking the model’s accuracy against the evaluation metrics is crucial.

Step 1- Data Debugging

Bad data quality affects the models’ performance; hence, checking the incoming data with expected values and schema is essential. Developers must ensure that the data splits are unbiased and represent the data distribution.

Step 2- Model Debugging

After checking the data quality, start debugging the model. Here are a few steps to consider.

• Check the Predictability of Features

Developers must check whether the data can predict the label correctly. Calculate the correlation metrics of the label and individual features to assess their predictive signal.

But, as developers cannot detect non-linear correlations, they must train and test the model using forms of cross-validation- with and without the feature. Also, selecting small examples for the models to learn reduces opportunities for other bugs.

• Build a Baseline Model

Having a simple baseline with a primary model development process is essential. This helps verify the model’s performance at specific levels.

Harness a simple linear model with no non-linear attributes, inclining the baseline towards the most common labels or mean values. A baseline with various complexity levels can help build a validation plan. It allows developers to see where the model fails to address the challenges.

• Devise Model Test Cases

Developers must build and run model-based test cases. A neural model, for instance, has multiple layers, each layer having neurons/units. They can have several test cases to validate the accuracy of the architecture model.

Step 3- Analyze Hyper-Parameters

The next step is to validate and adjust the hyper parameters. Developers can tweak these parameters during successive runs of training a model. These differ from the model’s trainable parameters that remain unchanged while training.

Here are a few model-specific parameters to consider:

• Batch Size 

For the Stochastic Gradient Descent (SGD) algorithm, the examples in a mini-batch are only 1. Begin with values between 10 and 1000 as the memory puts an upper bound on batch size.

• Epochs

No fixed optimal number of epochs exists as it relies entirely on the problem and data. Generally, the number of epochs needs to be 1.

• Regularization

Developers can add regularizations based on overfitting occasions. For linear models, they can use L1 regularization to minimize the model’s size.

It is always better to start with small regularizations. Hence, for better reproducibility and stability, use L2 regularization. For non-linear models, developers can use the dropout method for regularization.

• Depth and Width

Increasing depth or width increases the model’s complexity. Therefore, start with a non-deep model that has one to two layers. Then, developers can increase its depth linearly. However, the input and output layers’ widths are problem-dependent.

More importantly, the model’s width and depth need tuning to reach their optimum values depending on the problem.

Step 4- Track the Model’s Performance Metrics

In this step, developers must track the fundamental quantitative values to understand how the model would behave if something goes awry. Here are a few model metrics to consider:

• Loss and Accuracy

The loss number indicates how bad the model’s performance was on a single example. It helps understand whether the prediction aligns with the expectations. The ideal value is zero.

Accuracy shows the fraction of accurate predictions the model suggests. But, the metric is sometimes not as informative and misleading.

• Recall/Precision/AUC

In addition to the number of losses and accuracy, developers need more explainability and metrics for the results.

Precision helps understand what proportions of positive identifications were actually accurate. Recall helps understand what proportions of actual positives were identified accurately.

Area Under Curve (AUC) offers a total performance measure across all possible classification thresholds. It measures the model’s prediction quality irrespective of the classification threshold.

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Step 5- Debugging Loss Curves

Interpreting loss curves is challenging. There can be many fluctuations every time developers train or re-train the model.

How do these fluctuations occur? Can these be addressed? Here are a few instances that will help debug loss curves.

• How to Debug When the Model is Not Converging?

Received data does not always align with the expected schema. Therefore, developers must check the data schema skew and data values skew. These factors are vital since they can significantly change the statistical properties.

Moreover, simplify the model and ensure it trains on a smaller scale. Lastly, compare it with the baseline and then add complexity incrementally.

• How to Debug When the Loss Starts Increasing in a Normally Behaving Model?

The key reason for such an anomaly is inaccuracies in loss calculations. Developers must check for NAN values in inputs or from intermediate operations.

At the same time, check for data anomalies in the batches. Removing or ensuring equal distribution between batches is essential to minimize their effect.

• Training and Testing Loss Values Do Not Align. What to do? 

The cause of this issue is model overfitting. Developers must add regularization and make the model less complex. They can also check training and testing data splits for bias or misrepresenting different classes.

Conclusion

Debugging the machine learning (ML) model is challenging as developers must consider several factors to build high-performing ones. By addressing the core reasons for why a model is underperforming and identifying model failure patterns, it becomes easier to improve ML model performance.