Machine learning empowers the machine to perform the task autonomously and evolve based on the available data. However, while working on a Machine Learning algorithm, one may come across the problem of Underfitting or overfitting. Both these aspects can impact the performance of the Machine Learning model. Hence, in this blog, we are going to discuss how to avoid Underfitting and overfitting.
Overview of Underfitting and Overfitting
What is Underfitting in Machine Learning?
Training data plays an important role in deciding the effectiveness of an ML model. However, any error or flaw can impact the overall analysis. In the case of Underfitting training data, the model is not able to establish a correlation between the input and output variables.
Underfitting results primarily because the model is too simple to work on the available data, and hence the training time escalates. It may require more input features. Hence, it is not able to deduce the right outcomes resulting in flawed output.
What is Overfitting in Machine Learning?
Unlike Underfitting, in the case of Overfitting, the Machine Learning model is too advanced or has too much complexity. Thus, impacting the output. A Machine Learning professional would encounter more cases of Overfitting as compared to Underfitting. However, an Overfitting ML model can work on data but produces less accurate output because the model has memorized the existing data points and fails to predict unseen data. Hence, an overfitted model is not something that you should be looking at.
The best way to overcome the Underfitting issue is to focus on increasing the duration of training or by adding accurate inputs. Most of the time, to avoid the Underfitting issue, the ML expert ends up adding too many features to it, leading to Overfitting. It may result in low bias but high variance. It means that the statistical model fits closely against the training data. And hence it is not able to generalize the new data points.
Identifying Overfitting can be difficult because the training model performs with higher accuracy than an Underfitting model. In the next segment, we will be highlighting the strategies that will help you address the issue of Underfitting and Overfitting.
How to Avoid Overfitting in Machine Learning?
K-fold Cross Validation
ML experts use cross-validation to resolve the issue. For this, the dataset is divided into two categories: test and train data. Now the model is developed using the ‘train’ set. This model is tested to check the performance of the test data. This can help you identify when the model is overfitting and adjust the model accordingly.
Another way could be using the penalty term. The ML expert assigns a penalty for every loss action. Thus, it discourages the model from fitting the data too closely, thereby ensuring better generalization to new data.
You can also reduce the number of features in the model. Select only the important ones, and it can help in reducing the complexity of the model.
Combine Different Methods
This method involves reliance on combining multiple models to make predictions. This reduces the probability of Overfitting, thereby improving the performance of the model.
How to Avoid Underfitting in Machine Learning?
Although this method is effective in overcoming Overfitting, it can also be used to prevent Underfitting. You can reduce the strength of regularization, thus giving the model some leverage to fit the data.
Change the Model Architecture
You can also focus on changing the architecture of the model. For example, you can switch from a linear model to a non-linear one or a random forest. These models can easily work on capturing complex relationships in data.
Add More Features
The simplicity of training data can be a probable reason for Underfitting. To overcome this, you can add features and complexities to data.
Summary of Difference between Underfitting and Overfitting
From the above discussion, we can conclude that both Underfitting and Overfitting are two common challenges in ML. Thus, it can majorly impact the performance and accuracy of the model. The contributing reason for the same is the complexity of the model, which refers to the degree to which a model can capture patterns in the data.
In the case of Underfitting, the model is too simple to identify significant patterns, whereas, in the case of Overfitting, the model is too complex, leading to too much noise in the data, thus, nullifying the generalization.
Both Underfitting and Overfitting lead to poor generalization and high-test error. Consequently, the ML model is not able to give accurate predictions. In order to achieve a good balance between these two problems, it’s important to select a model with an appropriate level of complexity that can capture the underlying patterns in the data while avoiding fitting too closely to the noise.
The above discussion highlights the key difference between Overfitting and Underfitting. Both these issues can impact the performance of the ML model, and hence it becomes significant to carefully evaluate the data and use the right model architecture that can help in accurate output.
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If you have any further questions on Overfitting or Underfitting, drop your comments, and our experts will address them soon.
Let’s say you’re a researcher trying to build a model that predicts whether a person is likely to buy a certain product based on their age, income, and education level. You have a dataset with 1000 people’s information and whether they bought the product or not.
You split the dataset into a training set and a testing set, with 800 data points in the training set and 200 in the testing set. You train a model on the training set using a decision tree algorithm, and you achieve an accuracy of 90% on the training set and 75% on the testing set.
At this point, you may think you have a good model, since it has a high accuracy on both the training set and the testing set. However, you may be Overfitting your model to the training set.
To test this, you decide to create a validation set, with another 1000 data points. You then train your model again on the training set, but this time you tune the hyperparameters of the decision tree algorithm using the validation set. You achieve an even higher accuracy on the training set of 95%, and a slightly higher accuracy on the testing set of 78%.
But when you evaluate the model on the validation set, you find that the accuracy is only 60%. This is a clear indication of Overfitting.
The model was able to learn the patterns in the training set very well, but it failed to generalize to new data. This is because the model was too complex and fit the noise in the training data, rather than the underlying patterns.
To overcome Overfitting, you can try simplifying your model by reducing the number of features, reducing the complexity of the algorithm, or using regularization techniques.
Let’s say you’re a researcher trying to build a model that predicts the price of a house based on its size in square feet. You have a dataset with 1000 houses and their corresponding prices.
You split the dataset into a training set and a testing set, with 800 data points in the training set and 200 in the testing set. You train a linear regression model on the training set, and you achieve an accuracy of 60% on the training set and 55% on the testing set.
At this point, you may think that the model is not good enough since it has a low accuracy on both the training and testing sets. However, you may be Underfitting your model to the training set.
To test this, you decide to create a validation set, with another 1000 data points. You then train your model again on the training set, but this time you increase the complexity of the model by adding more features, such as the number of bedrooms and bathrooms. You achieve a slightly higher accuracy on the training set of 65%, but the accuracy on the testing set remains the same.
When you evaluate the model on the validation set, you find that the accuracy is still only 50%. This is a clear indication of Underfitting.
The model was too simple and couldn’t capture the underlying patterns in the data. This is because the model did not have enough complexity to fit the data well.
To overcome Underfitting, you can try increasing the complexity of your model by adding more features, using a more complex algorithm, or increasing the number of iterations