Artificial intelligence is becoming increasingly popular, especially with the rise of deep learning and machine learning. However, as new technologies emerge, they often face challenges during the learning process due to a lack of experience or insufficient knowledge. This can lead to confusion and unclear paths. Today, we're focusing on common mistakes that beginner machine learning engineers tend to make.
**Relying on Default Loss Functions**
At the beginning, the mean squared error (MSE) is often considered a safe default loss function. However, in real-world scenarios, this generic approach rarely leads to the best outcomes. For example, in fraud detection, where the goal is to minimize financial losses, the cost of false negatives (undetected fraud) varies significantly. Using MSE might give decent results, but it's not tailored to the actual business needs.
**Key Takeaway:** Always customize your loss function based on your specific objectives to achieve better performance.
**Using the Same Algorithm for All Problems**
Many beginners fall into the trap of using the same algorithm for every problem after completing introductory tutorials. They assume that one model works universally, which is a dangerous misconception. Different problems require different approaches, and relying on a single method can limit your results.
**Solution:** Let the data guide you. Try multiple models, evaluate their performance, and choose the one that fits best.
**Ignoring Outliers**
Outliers can be both valuable and misleading, depending on the context. In income forecasting, sudden changes might indicate meaningful trends, while others could result from data errors. Some models, like Adaboost, treat outliers as important, while decision trees may misclassify them.
**Key Takeaway:** Always analyze outliers carefully before deciding whether to remove or retain them.
**Incorrect Handling of Periodic Features**
Features like time (hours, days, months) are inherently periodic. Many beginners fail to convert these into meaningful representations. For instance, treating 23:00 and 00:00 as separate values can confuse the model.
**Best Practice:** Use sine and cosine transformations to represent periodic features as circular coordinates, preserving their natural relationship.
**Not Normalizing Features Before Regularization**
L1 and L2 regularization helps prevent overfitting by penalizing large coefficients. But without proper feature scaling, the regularization may unfairly penalize certain features, leading to biased results.
**Key Tip:** Always normalize your data before applying regularization to ensure fair treatment of all features.
**Misinterpreting Coefficients in Linear Models**
Beginners often assume that larger coefficients in linear regression or logistic regression indicate more important features. However, this isn't always true—coefficients can be influenced by feature scaling and multicollinearity.
**Important Note:** Feature importance should be evaluated through other methods, such as permutation importance or SHAP values, rather than relying solely on coefficient magnitudes.
While achieving good results in a project feels rewarding, it's crucial to remain vigilant. Even small oversights can affect model performance. The mistakes outlined here are just a few of the many subtle issues that can arise. By following best practices and double-checking your work, you can avoid common pitfalls and improve the quality of your machine learning solutions.
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