Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant:  Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant:  Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant:  Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant:  Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant:  Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant:  Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant:  Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant:  Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant:  Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant:  Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant:  Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant:  Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant:  Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant:  Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant:  Human: what is the difference between under-fitting and over-fitting

Assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

Human: How can you tell when a model is overfit?

Assistant: 
You would be able to tell when a model is overfit when it is has 'memorized' the training data. A symptom of an overfit model is if it performs great on the training data but cannot perform well on data that it has never seen before. A model is overfit it fits too closely to a training set of data, this resuts in the model not being abe to preform its job well on new images. If your mode has low error rates, and high variance, these are good signals of overfitting a model. To prevent this you should set aside a portion of testing images that are differnt from the rest to "test" for overfitting. If the images for training have a lower error rate, and the test images have a high error rate then your model is most likely overfitted. This means you might need to tailor your training!

If you have any questions do not be afraid to ask!