Underfitting and Overfitting in Machine Learning

Let us consider that we are designing a machine learning model. A model is said to be a good machine learning model, if it generalizes any new input data from the problem domain in a proper way. This helps us to make predictions in the future data, that data model has never seen.

Now, suppose we want to check how well our machine learning model learns and generalizes to the new data. For that we have overfitting and underfitting, which are majorly responsible for the poor performances of the machine learning algorithms.

A statistical model or a machine learning algorithm is said to have underfitting when it cannot capture the underlying trend of the data. (It’s just like trying to fit undersized pants!) Underfitting destroys the accuracy of our machine learning model. Its occurrence simply means that our model or the algorithm does not fit the data well enough. It usually happens when we have less data to build an accurate model and also when we try to build a linear model with a non-linear data. In such cases the rules of the machine learning model are too easy and flexible to be applied on such a minimal data and therefore the model will probably make a lot of wrong predictions. Underfitting can be avoided by using more data and also reducing the features by feature selection.

A statistical model is said to be overfitted, when we train it with a lot of data (just like fitting ourselves in an oversized pants!). When a model gets trained with so much of data, it starts learning from the noise and inaccurate data entries in our data set. Then the model does not categorize the data correctly, because of too much of details and noise. The causes of overfitting are the non-parametric and non-linear methods because these types of machine learning algorithms have more freedom in building the model based on the dataset and therefore they can really build unrealistic models. A solution to avoid overfitting is using a linear algorithm if we have linear data or using the parameters like the maximal depth if we are using decision trees.


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How to avoid Overfitting:

The commonly used methodologies are:

  • Cross- Validation: A standard way to find out-of-sample prediction error is to use 5-fold cross validation.
  • Early Stopping: Its rules provide us the guidance as to how many iterations can be run before learner begins to over-fit.
  • Pruning: Pruning is extensively used while building related models. It simply removes the nodes which add little predictive power for the problem in hand.
  • Regularization: It introduces a cost term for bringing in more features with the objective function. Hence it tries to push the coefficients for many variables to zero and hence reduce cost term.

  • Good Fit in a Statistical Model:

    Ideally, the case when the model makes the predictions with 0 error, is said to have a good fit on the data. This situation is achievable at a spot between overfitting and underfitting. In order to understand it we will have to look at the performance of our model with the passage of time, while it is learning from training dataset.

    With the passage of time, our model will keep on learning and thus the error for the model on the training and testing data will keep on decreasing. If it will learn for too long, the model will become more prone to overfitting due to presence of noise and less useful details. Hence the performance of our model will decrease. In order to get a good fit, we will stop at a point just before where the error starts increasing. At this point the model is said to have good skills on training dataset as well our unseen testing dataset.


This article is attributed to GeeksforGeeks.org

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