Artificial intelligence is becoming increasingly popular, especially with the rise of deep learning and machine learning. However, as beginners dive into this field, they often face challenges due to limited experience or a lack of technical knowledge. This can lead to confusion during the learning process, making it hard to stay on track. Today, we're focusing on common mistakes made by new machine learning engineers.
**Using the default loss function without customization**
At the beginning, the mean squared error (MSE) is a good starting point for a loss function. But when dealing with real-world problems, using a generic loss function might not yield the best results. For example, in fraud detection, you want to penalize false negatives based on the actual financial loss caused by fraudulent activity. While MSE may provide acceptable results, it's not the optimal choice in such scenarios.
**Key takeaway**: Always tailor your loss function to match your specific goals and business needs.
**Relying on a single algorithm for all tasks**
Many beginners tend to use the same algorithm for every problem after completing an introductory course. This approach is flawed because different algorithms perform better under different conditions. Relying on one method can limit the potential of your model.
**Solution**: Experiment with multiple models after preprocessing your data. This will help you identify which models work best for your particular dataset.
**Ignoring outliers**
Outliers can be either meaningful or noise, depending on the context. In some cases, like income forecasting, sudden changes might indicate important events that should be analyzed. On the other hand, if an outlier is due to data entry errors, it’s safe to remove it.
From a modeling perspective, some algorithms are more sensitive to outliers than others. For instance, Adaboost treats outliers as important examples, while decision trees may misclassify them.
**Key takeaway**: Always analyze your data carefully before starting any modeling process. Determine whether outliers should be kept or removed.
**Not handling periodic features properly**
Time-based features like hours, days, or months are inherently periodic. Many beginners fail to represent these features correctly, leading to poor model performance.
For example, when working with time data, converting hours into sine and cosine values helps capture the circular nature of time. This way, 23:00 and 00:00 are treated as close points on a circle, rather than being seen as far apart.
**Key takeaway**: Properly encoding periodic features is essential to avoid introducing irrelevant or misleading information into your model.
**Failing to normalize features before applying regularization**
L1 and L2 regularization are widely used to prevent overfitting in linear and logistic regression. However, many engineers neglect to standardize their features first. Without normalization, features with larger scales may be unfairly penalized, leading to biased results.
**Key takeaway**: Always normalize your data before applying regularization to ensure fair treatment of all features.
**Misinterpreting feature importance based on coefficient magnitude**
Some beginners assume that larger coefficients in linear models indicate more important features. This is incorrect, as coefficient sizes depend on the scale of the variables. Additionally, collinearity between features can distort these interpretations.
**Key takeaway**: Feature importance should be assessed using more reliable methods, such as permutation importance or SHAP values, rather than relying solely on coefficient magnitudes.
Working on machine learning projects and achieving good results is rewarding. However, just like in any industry, small details can have a big impact. The mistakes mentioned here are not exhaustive, but they highlight the importance of attention to detail and careful validation. To achieve accurate and reliable results, always follow a structured process and double-check your work to avoid common pitfalls.
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