Artificial intelligence is becoming increasingly popular, especially with the rise of deep learning and machine learning. However, as new technologies develop, they often face challenges due to limited experience or incomplete understanding. This can lead to confusion during the learning process, with unclear directions. Today, we’re discussing common mistakes made by beginners in machine learning.
**Relying on the Default Loss Function**
At the beginning, it's common to use mean squared error as a default loss function. While this works well for simple problems, real-world scenarios often require more tailored approaches. For example, in fraud detection, the cost of false negatives (undetected fraud) can be significant. A default loss function might not reflect this, leading to suboptimal results.
**Using the Same Algorithm for Every Problem**
Many beginners stick to one algorithm after completing tutorials, assuming it’s the best fit for all tasks. This approach is flawed because different problems may require different models. The key is to let the data guide you—try multiple models, compare their performance, and choose the best one.
**Ignoring Outliers**
Outliers can be misleading or informative, depending on the context. In income forecasting, sudden changes might signal important events. But if an outlier is due to data errors, it should be removed. Some models, like Adaboost, are sensitive to outliers, while others, like decision trees, might treat them as noise. Always examine your data carefully before deciding whether to keep or remove outliers.
**Not Handling Periodic Features Correctly**
Features like time, wind direction, or temperature have periodic patterns. If not handled properly, these features can confuse the model. For instance, using hour values directly might mislead the model into thinking 23:00 and 00:00 are far apart. Instead, convert them using sine and cosine functions to represent them on a circular scale, preserving the relationship between 23:00 and 00:00.
**Forgetting to Normalize Before Regularization**
L1 and L2 regularization helps prevent overfitting by penalizing large coefficients. However, if the features are not normalized, the regularization will be biased. For example, if one feature is in dollars and another in cents, the model will unfairly penalize the smaller unit. Always normalize your data before applying regularization.
**Using Absolute Coefficients to Judge Feature Importance**
Some beginners assume that larger coefficients in linear or logistic regression mean more important features. This is incorrect because coefficients depend on the scale of the input variables. Collinearity can also distort this interpretation. It’s better to use other methods, like permutation importance or SHAP values, to assess feature impact accurately.
While achieving good results feels rewarding, it’s crucial to pay attention to the details. These mistakes are common but avoidable with careful practice. Always follow a structured process, double-check your work, and stay aware of the subtle issues that could affect your model’s performance. By doing so, you’ll be on the right path to building effective and reliable machine learning systems.
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