Common Machine Learning Mistakes and How to Avoid Them

In our current era of data science and technology in the world, machine learning has gained a lot of space. Those reflexes can still trap experienced practitioners sometimes which in turn results in faulty models and suboptimal performance. Here, we are going to do a little digging into the most common mistakes when it comes to machine learning — and give you some hands-on advice on how not commit them.

1. Overfitting and Underfitting

• Overfitting: building a model that is too complicated and learns the training data so well that it has trouble with unseen, new data.
• Underfitting : If your model is too simple and cannot indeed mimic the underlying data generating process then it will not perform well either on train OR test sets.
• How to Avoid:
  • Regularization: Regularization is a technique (constraint added) used on the input of weight with cost function to avoid overfitting. L1 and L2 regularization are two common techniques which can be used wherein these functions can restrict complex models.
  • Karma Points: Cross Validation, Split your data in training and validation set to see how model perform on unknown data.
  • Feature engineeirng — Building useful and informative features that will help the model perform better.

2. Data Leakage

• Data leakage: the use of information from the testing set in training, resulting too-good-to-be-true performance metrics
• How to Avoid:
  • Practical data splitting : One important point is that the same set of images should not be in both training and testing sets
  • The time-based splits: If it is a Time-Series data set, make sure that the training data comes well before in point of own to the test data.
  • Shuffle split: Shuffle data before splitting to restrict accidental information leakage.

3. Ignoring Data Quality

• Poor data quality; inaccurate, missing or inconsistent data can ruin the model alliances.
• How to Avoid:
  • Data cleaning – detect and correct (or remove) non-accurate/faulty data such as incompleteness, inconsistency or irrelevant components
  • Normalize and standardize data — so that features are on a comparable scale.
  • Data augmentation: Creating more and variation in training data to make a model robust and keep it alive if there is scarcity of the quantity.

4. Over-reliance on Metrics

• Allowing a single metric to dominate your tracking (metric bias) can lead you down the garden path.
• How to Avoid:
  • Multi Metric : Employ multiple metrics to gauge the model performance, particularly using accuracy precision recall and f1 score.
  • Principle 4: Domain Knowledge — Remember to select metrics based on your application needs

5. Neglecting Interpretability

• Black box models – Some machine learning techniques (like deep neural networks) are considered black boxes, meaning that it is difficult to interpret them.
• How to Avoid:
  • Interpretability of AI: Use tools like feature importance, SHAP values and LIME to explain the decision algorithms made.
  • Simplify model: Think about simpler more interpretable (e.g. decision trees or linear regression) possible alternatives to your current approach

6. Ignoring Bias and Fairness

• BiasIf the data used for training contains biased information, machine learning models will learn those biases.
• How to Avoid:
  • Use diverse datasets: lower bias by using many and representative dataset.
  • What can you do to prevent these? You might consider bias detection, and fair metrics in your model.
  • Reduce sample complexity: Implement bias mitigation strategies (e.g. reweighting, adversarial training).
• Data scientists can deliver better models with higher predictive accuracy, greater reliability and fairness when they understand and mitigate these common machine learning pitfalls.