Training AI models is all about teaching machines to learn from data, allowing them to analyze patterns, make predictions, and automate tasks. But this process is not as easy as it sounds. Several factors require your attention in this regard, as even a minor oversight can affect the accuracy and effectiveness of the resulting AI model.
In this article, we will discuss some commonly committed mistakes in machine learning and their solutions to reduce or avoid them.
- Overfitting and Underfitting the Model
Sometimes your AI model fails to strike the right balance between learning patterns and generalizing to new data. This is when overfitting and underfitting occur. Whether your model has memorized the data rather than learning patterns, or it has not learned enough from its training, both situations are serious problems.
Solution
Using cross-validation to test the AI model on multiple subsets of your data can provide a robust performance assessment. Moreover, you can use regularization techniques to penalize model complexity.
- Using Poor Quality or Biased Data
In training AI models, the quality of data makes a significant difference, and the overall success of your model depends on this factor. You should never use poor data, such as records that are incomplete, inconsistent, or inaccurate, as they will produce unreliable and incorrect model outputs.
On the other hand, biased data causes an AI model to perpetuate or amplify harmful stereotypes. For example, if you train an AI model on historical data where male applicants were favored, the model will not only learn but also repeat this bias.
Solution
You must establish a strong data governance strategy to ensure data is relevant, complete, and accurate. After that, perform a bias audit on your dataset. It is essential to find imbalances that can affect fairness and inclusivity.
For the best results, use diverse data sources. Additionally, include a representative range of examples to make your model more robust and adaptable to real-world scenarios. Companies like Intuit offer AI roles where you can get first-hand experience in configuring AI models on a consistent basis to grow your competencies in the field.
- Improper Hyperparameter Tuning
Hyperparameters are configurations that control the training of your model. Choosing incorrect values for this critical process can degrade performance. A high learning rate can cause the AI model to overshoot optimal performance, while a low learning rate can result in slow convergence.
Solution
To find the best hyperparameter configuration, you must implement a proper tuning strategy. Use techniques, such as grid search or Bayesian optimization.
For hyperparameter tuning, it is advisable to start with default settings. It helps establish a performance baseline before making extensive adjustments.
- Allowing Data Leakage
If your AI model is performing unrealistically well during validation but fails disastrously in production, data leakage could be the reason. This situation occurs when you use information outside of the training data. Therefore, you should never use future information or skip any step in the data preprocessing.
Solution
You must always split your data into three categories: training, validation, and test sets. Additionally, use pipelines to ensure that all preprocessing steps are fitted exclusively on training data.
