Overfitting can occur due to a variety of reasons in cancer research:
1. High Dimensionality: Cancer datasets often have a high number of features (e.g., genetic markers, patient demographics, clinical measurements) but a relatively small number of samples. This high dimensionality can lead to overfitting. 2. Noise and Outliers: Cancer data can include significant noise and outliers due to measurement errors or biological variability. Models that are too complex can start to fit these noise and outliers. 3. Small Sample Sizes: Limited availability of patient data can cause models to overfit since they do not have enough examples to learn the general patterns.
