3 Common Data Handling Errors That Can Ruin Your Machine Learning Model

In the rapidly evolving field of machine learning, even seasoned professionals can overlook subtle errors that significantly degrade model performance. A recent analysis highlights three critical data handling mistakes that can occur during both preprocessing and modeling phases. Firstly, data leakage can inflate model accuracy by 10-20% when training data inadvertently includes information from the test set. Secondly, inadequate handling of missing data can lead to biased models, with studies showing that ignoring missing data can reduce model accuracy by up to 15%. Lastly, feature engineering errors like improper scaling or normalization can skew results, potentially decreasing model performance by 5-10%. These errors, if not addressed, can compromise the predictive power, reliability, and applicability of machine learning models.

Source: towardsdatascience.com