Artificial intelligence is becoming increasingly widespread, with deep learning and machine learning gaining significant traction. However, when it comes to the development of new technologies, especially for beginners, the learning process can often be chaotic due to lack of experience or insufficient knowledge. Without a clear direction, it's easy to fall into common pitfalls. Today, we're going to discuss six major mistakes that novice machine learning engineers often make.
**Using the default loss function without customization**
At the beginning, mean squared error is often considered a safe default choice for a loss function. However, in real-world scenarios, this generic approach rarely leads to optimal results. For example, in fraud detection, where the goal is to minimize financial loss, a custom loss function that penalizes false negatives based on the actual dollar amount lost is more effective than using mean squared error.
Key takeaway: Always tailor your loss function to match your specific business goals and problem requirements.
**Applying the same algorithm to all problems**
Many beginners, after completing introductory tutorials, tend to use the same algorithm across all tasks. This approach is not only inefficient but also limits the potential of their models. Different algorithms perform better under different conditions, so it’s crucial to test multiple models and let the data guide your choice.
Tip: Experiment with various algorithms and let performance metrics help you decide which model works best for your dataset.
**Ignoring outliers without proper analysis**
Outliers can either be critical insights or mere noise, depending on the context. In income forecasting, for instance, sudden changes might signal important trends, while in other cases, they may result from data entry errors. Some models, like Adaboost, are sensitive to outliers, while others, such as decision trees, may treat them as misclassifications.
Important: Always investigate outliers before deciding whether to remove them. Understanding their source can improve model accuracy.
**Failing to handle periodic features properly**
Features like time of day, wind direction, or month of the year are inherently periodic. If not handled correctly, they can confuse the model. For example, treating 23:00 and 00:00 as completely separate values can lead to incorrect assumptions. A better approach is to convert these features using sine and cosine transformations, representing them as points on a circle.
Note: Properly encoding periodic features ensures the model understands the cyclical nature of the data.
**Neglecting feature normalization before regularization**
L1 and L2 regularization are powerful tools to prevent overfitting, but they work best when features are on a similar scale. Without standardization, features with larger ranges (e.g., transaction amounts in dollars vs. cents) will be penalized disproportionately, leading to biased results.
Pro tip: Always normalize or standardize your features before applying regularization techniques.
**Relying solely on coefficient magnitude for feature importance**
Some beginners assume that larger coefficients in linear models indicate more important features. However, this is misleading because coefficient size depends on the scale of the feature. Additionally, collinearity between features can distort the interpretation of coefficients.
Bottom line: Feature importance should be assessed through more reliable methods, such as permutation importance or SHAP values, rather than relying solely on coefficient magnitude.
While achieving good results on a project is rewarding, it's essential to pay attention to the small details that can impact performance. The mistakes outlined here are just a few of the common issues that even experienced engineers may overlook. By following best practices and double-checking your work, you can avoid these pitfalls and build more accurate, reliable models.
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