Overfitting Vs. Underfitting | Machine Learning
Overfitting occurs when a model is trained too well on the training data, and as a result, it performs poorly on unseen or new data. It occurs when the model is too complex and captures the noise in the training data. For example, a model that uses polynomial functions of degree 15 to fit a dataset of 100 observations would likely overfit the data.
Underfitting, on the other hand, occurs when a model is not complex enough to capture the underlying pattern in the data. It occurs when the model is too simple and not able to capture the nuances in the data. For example, a model that uses a linear function to fit a dataset that is generated from a non-linear function would underfit the data.
Overfitting:
A student who memorizes the answers to all of the questions on a practice test, but performs poorly on the actual exam because the questions are different.
A chef who uses too many ingredients and complex techniques to create a dish, resulting in a dish that is too overwhelming for most people to enjoy.
Underfitting:
A student who does not study enough for an exam and performs poorly because they did not learn the material well enough.
A chef who uses only a few simple ingredients and techniques to create a dish, resulting in a dish that is too bland and not flavorful enough.
GitHub Link: https://github.com/PRIYANG-BHATT/Data...
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