What is overfitting in machine learning?
Overfitting occurs in machine learning when a model learns not only the underlying patterns in the training data but also the noise and random fluctuations. This results in a model that performs exceptionally well on the training data but fails to generalize to new, unseen data. Overfitting is often a sign that the model is too complex, with too many parameters relative to the amount of training data available.
The primary symptoms of overfitting include a high accuracy on the training set but poor performance on the validation or test set. This issue can arise from training a model for too long or using a model that is overly complex for the given task. Overfitting can be mitigated by using techniques such as cross-validation, pruning, regularization, or simplifying the model.
Ultimately, the goal in machine learning is to develop models that generalize well to new data, striking the right balance between underfitting and overfitting. Understanding and addressing overfitting is crucial for creating robust and reliable models that perform well in real-world applications.
To deepen your understanding of this and other concepts, consider enrolling in a machine learning data science course.
Shrey Maan
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Ruhi Parveen commented
Overfitting in machine learning occurs when a model learns the training data too well, capturing noise and anomalies rather than general patterns. This results in high accuracy on the training set but poor performance on unseen test data, as the model fails to generalize to new situations. Overfitting often arises with overly complex models relative to the amount of data, such as those with too many parameters. To mitigate overfitting, techniques like cross-validation, regularization, pruning, and simplifying the model are used to ensure the model performs well on both training and test datasets.
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khushnuma commented
Overfitting in machine learning occurs when a model learns the training data too well, capturing noise and outliers instead of the underlying pattern. This results in high accuracy on the training set but poor performance on unseen test data because the model fails to generalize. Overfitting happens when the model is too complex relative to the amount of data, such as having too many parameters or layers. Techniques to combat overfitting include simplifying the model, using regularization methods, employing cross-validation, and augmenting the training data.