12 Questions to Ask Yourself When Your Machine Learning Model is Underperforming
When your ML model is failing to meet performance expectations, resist the urge to immediately tweak hyperparameters or change the architecture. Instead, take a step back and perform a structured diagnosis. Most real-world ML problems stem from issues with the data—not the model itself. In fact, according to our Head of Research Kamil Szelag, PhD, data scientists often spend 80% of their time preparing and refining datasets, and only 20% on model development and tuning. Below is a practical, technical checklist designed to help you debug underperforming models and realign development efforts more effectively.
1. Do I understand the problem?
Before building any model, ensure you have a clear understanding of the real-world problem it’s trying to solve. Misalignment between the problem and your model objective leads to failure in deployment. Ask yourself: what does success look like in the real world? A strong grasp of the business and operational environment will help shape better data choices and evaluation strategies. This means that you understand the “why” and “how” behind the system, not just the data and the model.
2. Am I leveraging domain knowledge?
Without domain expertise, you risk creating technically accurate but practically irrelevant datasets, leading to models that perform well in benchmarks but fail in real-world deployment. Domain knowledge also ensures that class boundaries, correlations, and edge cases are properly reflected in the training data.
Even with pixel-perfect labels—especially in synthetic data—domain knowledge is essential. It informs critical decisions about which classes to include, how to define them meaningfully, which scenarios and edge cases to simulate, and how to validate the realism of synthetic samples.
3. Is my training and testing data of a high quality?
Check for signal-to-noise ratio, label leakage, resolution mismatches, and modality inconsistencies. High-quality data enables the model to learn relevant patterns rather than noise or artifacts. Low-quality data can introduce spurious correlations or lead the model to learn shortcuts that don't generalize.
4. Are the data labels correct and consistent?
Inaccurate, inconsistent, or ambiguous labels mislead your loss function. Frequent audits and inter-annotator agreement metrics can reveal hidden issues. Even small inconsistencies can have large impacts, especially in small datasets or rare class scenarios.
5. Is the metadata correct and consistent?
Incorrect timestamps, corrupted geolocation, or mismatched sensor metadata can introduce spurious patterns and degrade model performance. Metadata issues are especially problematic when training on time series or multi-modal datasets. Ensure metadata aligns with the intended use case and model input expectations.
6. Is the dataset large enough?
Insufficient training data leads to high variance and poor generalization. Consider data augmentation, synthetic data, or transfer learning to address data scarcity. Increasing the diversity and volume of data can uncover patterns the model would otherwise miss. For example, you may need to add more renders to your training dataset, to improve gaze vector detection and emotion recognition.
7. Do I have enough case representation in the dev dataset?
Ensure the validation and test sets include rare classes and edge cases to get a realistic estimate of production performance. Skewed representation here leads to overconfidence in models that won’t scale in production. Pay special attention to the class distribution and edge case variety across all subsets.
8. Is my model overfitting?
Overfit models will fail to generalise to unseen samples. There are multiple regularization methods used during the development of ML algorithms, but the most widely used include the following:
L1 (Lasso) and L2 (Ridge) Regularization
These methods add a penalty to the loss function based on model weights. L1 encourages sparsity (zeroes some weights), while L2 discourages large weights. Both help the model focus on the most relevant features. Regularization is a guardrail against learning spurious correlations, especially in high-dimensional spaces.
Dropout (for neural networks)
Dropout randomly disables a fraction of neurons during training. Prevents over-reliance on specific paths, improving generalization. Dropout forces the model to learn redundant representations and improves robustness.
Early Stopping
Stops training when validation performance ceases to improve. Reduces the risk of overfitting and helps find the optimal training iteration before memorization starts and performance begins to decline.
Data Augmentation
Alters training samples (rotation, noise, occlusion) to increase diversity. Synthetic data can play a major role here by simulating rare or hard-to-capture conditions. Augmentation increases effective dataset size and improves robustness to real-world variability.
Weight Regularization in Deep Learning Frameworks
Most frameworks (e.g., TensorFlow, PyTorch) support easy integration of weight decay (L2 regularization). Implementing it correctly can drastically reduce generalization error. Regularization methods are easy to apply and critical for preventing overfitting in deep models.
9. Is the model architecture suitable for the problem?
Different tasks require different inductive biases—don’t use a hammer when you need a scalpel. CNNs are great for images, transformers for sequences—choose architectures based on your data type and task complexity. The right architecture aligns structural assumptions with the nature of your inputs. And if you have difficulties with figuring out which architecture is best suited to your needs, consider consulting the literature or data scientist communities—it is possible that someone had a similar problem and was able to solve it. No need to reinvent the wheel.
10. Am I using too few or too many parameters?
Underparameterized models can’t capture the complexity of the data; overparameterized models may overfit. Aim for a balance where the model complexity matches the available data and task variance. Use validation loss curves and model capacity experiments to guide your choices.
11. Am I using features relevant to the problem?
Use feature selection or importance ranking methods to ensure your input variables contribute meaningfully to predictions. Irrelevant or redundant features often lead to noise and reduce model interpretability. Pruning these features can improve both generalization and training speed. On the other hand, through feature engineering, more complex relationships in the data can be generated to represent the data more effectively.
12. Has the test data changed compared to the dev or training data?
Check for data drift or distributional shift—e.g., new sensor configurations, environment conditions, or label definitions. If the input or label distribution shifts over time, your model’s predictive performance will degrade. Use tools for continuous monitoring and retraining pipelines to adapt, such as Azure DevOps, AWS SageMaker, and MLflow.
Final Thoughts
Debugging ML models is a disciplined, iterative process. Understanding the data—its quality, structure, and domain context—is far more impactful than endless tuning of model hyperparameters. At SKY ENGINE AI, we design high-fidelity synthetic data solutions that empower data scientists to overcome these challenges, accelerate experimentation, and improve real-world deployment outcomes.