Question

Difference between overfitting and underfitting in machine learning

Answer 1

Answer:

statistics and machine learning, one of the most common tasks is to fit a model to a set of training data, so as to be able to make reliable predictions on general untrained data.

In overfitting, a statistical model describes random error or noise instead of the underlying relationship. Overfitting occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. A model that has been overfit has poor predictive performance, as it overreacts to minor fluctuations in the training data.

Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data. Underfitting would occur, for example, when fitting a linear model to non-linear data. Such a model too would have poor predictive performance.

Answer 2

Difference between overfitting and underfitting are given below:-

Explanation:

• Overfitting happens when the design is two dimensional, including providing so many constraints relative to the number of measurements. whereas Underfitting occurs when a mathematical model or an automated genetic algorithm can not capture the database schema pattern.
• The approach to prevent overfitting is to use a regression whether we have vector information or if we use maximum depth of dimensions if we have decision trees. Underfitting can be avoided when using further software and also by growing functions when choosing a device.
• If a model is equipped with so many results, it starts to learn from the noise and inaccurate data entries in our data set. The template does not classify the information correctly due to excessive information and interference. whereas Underfitting is losing the precision of our machine learning platform. It simply implies that our model or implementation does not match the data well enough.

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