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What is Overfitting & Underfitting in Machine Learning?
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In this video, we are going to cover the difference between overfitting and underfitting in machine learning.
Machine learning is the art of creating models that are able to generalize and avoid memorization.
Model that performs well during training and testing (on new dataset that has never seen before) is considered the best model (goal).
AI Models are considered underfitting the data if they are too simple and cannot reflect the complexity of the training dataset. We can overcome under fitting by: (1) increasing the complexity of the model, (2) Training the model for a longer period of time (more epochs) to reduce error
AI models overfit the training data when it memorizes all the specific details of the training data and fail to generalize.
Overfitting models tend to perform very well on the training dataset but poorly on any new dataset (testing dataset)
#machinelearning #overfitting #underfitting #
Machine learning is the art of creating models that are able to generalize and avoid memorization.
Model that performs well during training and testing (on new dataset that has never seen before) is considered the best model (goal).
AI Models are considered underfitting the data if they are too simple and cannot reflect the complexity of the training dataset. We can overcome under fitting by: (1) increasing the complexity of the model, (2) Training the model for a longer period of time (more epochs) to reduce error
AI models overfit the training data when it memorizes all the specific details of the training data and fail to generalize.
Overfitting models tend to perform very well on the training dataset but poorly on any new dataset (testing dataset)
#machinelearning #overfitting #underfitting #
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