Overfitting in Machine Learning
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.
Underfitting in Machine Learning
Underfitting refers to a model that can neither model the
training data nor generalize to new data.
An underfit machine learning model is not a suitable model and
will be obvious as it will have poor performance on the training data.
Underfitting is often not discussed as it is easy to detect
given a good performance metric. The remedy is to move on and try alternate
machine learning algorithms. Nevertheless, it does provide a good contrast
to the problem of overfitting.
With its wealth of beneficial content, this article is extremely valuable. Discover additional insights by clicking this link.SEO Company in Chennai
ReplyDelete