Question for for Data Science for Python What is model overfitting? A situation
ID: 3825829 • Letter: Q
Question
Question for for Data Science for Python
What is model overfitting?
A situation when the Ordinary Least Square regression is fitted with very high R2.
A situation when the model has too many features.
A situation when the model "memorizes" each item used for its training.
A situation when k-mean clustering algorithm produces too many clusters.
a.A situation when the Ordinary Least Square regression is fitted with very high R2.
b.A situation when the model has too many features.
c.A situation when the model "memorizes" each item used for its training.
d.A situation when k-mean clustering algorithm produces too many clusters.
Explanation / Answer
c.
A situation when the model "memorizes" each item used for its training.
Overfitting refers to a model that models the training data too well.
Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model. The problem is that these concepts do not apply to new data and negatively impact the models ability to generalize.
Overfitting is more likely with nonparametric and nonlinear models that have more flexibility when learning a target function. As such, many nonparametric machine learning algorithms also include parameters or techniques to limit and constrain how much detail the model learns.
For example, decision trees are a nonparametric machine learning algorithm that is very flexible and is subject to overfitting training data. This problem can be addressed by pruning a tree after it has learned in order to remove some of the detail it has picked up.
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