Everyone that has trained machine learning models has faced this: Its performance is worse than expected, and you are confused about how to improve its performance. Of course, it’s a skill you perfect over time, but here are some ways to help you achieve better accuracy with your model. Let’s find out about those methods in this article.
Table of Contents
- Identify the cause of your model’s underperforming
- Collect more data
- Preprocess your data
- Manipulate the features
- Try different models
- Optimize the training
1. Identify the Cause of Your Model’s Underperforming
First of all, if your model is worse than expected, you must find its cause. Make sure you do a train-test split before you dive into the issue. This helps you assess the model’s performance to find out how it reacts to unseen data. There are three cases where the model is fitted to a dataset.
- Optimal fitting
The first scenario, optimal fitting, is excellent. The model has learned from the dataset and has gained the ability to generalize it onto novel information that it receives. As a result, you should see a reasonable accuracy and validation accuracy for your model. Therefore, if you are struggling to enhance the model, it’s probably not optimally fitted.
The second scenario, underfitting, means that the model fails to capture the patterns of the dataset and cannot apply its knowledge to other data in the real world. If you see a low accuracy and validation accuracy, you should be facing this problem.
The third scenario, overfitting, means that the model has captured so many hidden conventions in the dataset that some of them are not present or less influential in the real world. Therefore, when the model faces new data, its accuracy is lowered. If your model has a high training accuracy but low validation accuracy, the culprit is likely overfitting.
Now, after examining why you wreaked havoc on your model, let’s explore how you will fix it.
2. Collect More Data
First of all, make sure you have enough data in your dataset. If you only have a small amount of data, like just 10 data points, your model will not be able to figure out the relationships between the features and the labels. Therefore, consider enlarging your dataset and collecting more data if you are underfitting. You can do so in many ways, such as using APIs, web scraping, and conducting interviews and surveys.
However, if you are overfitting, ensure that your data is balanced and diverse. If it’s not, add more different types of data that it will encounter in the real world. In that case, the model will generalize over most situations, improving its performance.
3. Preprocess Your Data
Other than quantity, quality is also important for training your machine learning model. Therefore, it is often necessary to preprocess your data, where you make it easier to digest and analyze by your model. These steps include:
- Dealing with missing data
- Removing duplicates and outliers
- Balance out the categories
- Data augmentation
We have written an article summarizing the steps you should take among these processes. Check out here if you want to learn more about preprocessing data effectively.
4. Manipulate the Features
If you are confident that your data is quality, recall what you selected as features. If you’re overfitting, some of them may not be relevant, causing extra noise inside your data and more possibility for your model to overfit against the dataset. On the other hand, if you’re underfitting, search for any features you didn’t include. There may be some other features in your dataset related to the final result, which can help your model capture the obscure patterns in the dataset.
If you’re still not getting good results, consider feature engineering. Deep learning models automate that time-consuming process, but if you’re using other models, you’ll have to do it yourself. Feature engineering means deriving new features for the model to be trained based on already-existing features. While this doesn’t increase the amount of data, it may help exaggerate the features that influence the result, thus improving the model’s accuracy.
5. Try Different Models
If you’re still getting problems at this stage, it’s possible that the model you’re using is simply not good enough and is not made for datasets like yours. In that case, it’s better to choose a different model. Deep learning models are often better than classical machine learning models, as they can fit the dataset more flexibly and optimally because of their large number of interconnected neurons.
6. Optimize Training
Last but not least, you should optimize the training process of your model. When a model is being trained with different parameters, it comprehends and learns from the dataset differently such that its predictions will also be different.
If you’re overfitting, consider decreasing the complexity of the model. In that case, it can’t fit into the dataset as closely, but it prevents the model from overfitting. You can also add more dropout and regularization layers to abandon parts of a neural network periodically to reduce model complexity autonomously or add early stopping functionalities to ensure that training is stopped after the model struggles to improve its accuracy.
If you are underfitting, do the opposite of what you’ll do when you are overfitting. Add more layers to increase the complexity of your model, decrease the number of dropout layers, and train the model for more epochs to achieve a longer training time. This makes sure that the model can capture more patterns from the dataset.
In this article, we have explained how we should improve the performance of your machine learning model, including identifying whether your model is overfitting or underfitting and finding ways to improve your data and your model training so that your model can perform better the next time you train it. Remember that it may take some skill to achieve this, as the situation differs in every dataset. Also, if we have missed any points we should have covered, please leave them in the comments below.