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What is underfitting and overfitting?

Your model is overfitting your training data when you see that the model performs well on the training data but does not perform well on the evaluation data. This is because the model is memorizing the data it has seen and is unable to generalize to unseen examples.

What is meant by overfitting in machine learning?

Overfitting means creating a model that matches (memorizes) the training set so closely that the model fails to make correct predictions on new data. An overfit model is analogous to an invention that performs well in the lab but is worthless in the real world.

What is overfitting and why is it bad?

Overfitting is an undesirable machine learning behavior that occurs when the machine learning model gives accurate predictions for training data but not for new data. When data scientists use machine learning models for making predictions, they first train the model on a known data set.

What is the best way to explain overfitting?

In machine learning, overfitting occurs when an algorithm fits too closely or even exactly to its training data, resulting in a model that cant make accurate predictions or conclusions from any data other than the training data.

What is meant by overfitting?

In machine learning, overfitting occurs when an algorithm fits too closely or even exactly to its training data, resulting in a model that cant make accurate predictions or conclusions from any data other than the training data. Overfitting defeats purpose of the machine learning model.