In a scenario where a machine learning model is showing unexpectedly high training time, how could incorrect handling of missing data be a factor?
- Missing data might have created outliers in the data.
- Missing data might have increased the complexity of the model.
- Missing data might have increased the dimensionality of the data.
- Missing data might have introduced multicollinearity in the data.
Incorrectly handling missing data (such as one-hot encoding missing values) can increase the dimensionality of the dataset, leading to a longer training time due to the curse of dimensionality.
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