Explain the concept of underfitting and why it occurs in machine learning models.

Underfitting is a phenomenon that occurs in machine learning models when the model fails to capture the underlying patterns and relationships present in the data. It is characterized by high bias and low variance, resulting in a model that is too simple to accurately represent the complexity of the data. In this explanation, we will

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What is overfitting in machine learning models and how can it be identified?

Overfitting is a common problem in machine learning models that occurs when a model performs extremely well on the training data but fails to generalize well on unseen data. In other words, the model becomes too specialized in capturing the noise or random fluctuations in the training data, rather than learning the underlying patterns or

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