Major Problems of Machine Learning Datasets: Part 3

Machine learning datasets often face challenges such as imbalanced data, high dimensionality, and non-normal distribution, each affecting model performance in different ways. Imbalanced data, where one class significantly outnumbers the other, can bias a model towards the majority class, leading to poor precision and recall for the minority class; techniques like stratified sampling and resampling with tools like SMOTETomek help mitigate this issue. High-dimensional data, which increases training time and complicates visualization, can be addressed through dimensionality reduction techniques such as Principal Component Analysis (PCA), which reduce feature numbers while preserving variation. Non-normal distribution of data impacts model accuracy, and transformations such as log-normal and Box-Cox can convert skewed data into a more normal distribution, enhancing model predictions.

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