Risks and limitations of machine learning
As much as machine learning has revolutionized various aspects of business and society, it’s essential to recognize that it comes with risks and limitations. Understanding these can guide decision-makers to take better, more informed actions and mitigate potential negative consequences.
Overfitting and underfitting
Overfitting occurs when a model learns the training data too well. It becomes so engrossed in the specific details and noise in the training set that it performs poorly on unseen data. An overfitted model has a low bias but a high variance.
On the other hand, underfitting happens when a model is too simple to capture all the relevant relationships in the data. It may perform poorly on both the training data and unseen data. An under-fitted model has a high bias but low variance.
Balancing the trade-off between overfitting and underfitting is critical in creating a model that generalizes well to unseen data.
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