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Are you facing problems in improving model performance during testing phases? Even if you improve the model, it fails miserably in production due to unknown reasons. If you are facing similar issues, you are at the right place.
In this blog, I will share 7 tips to make your model accurate and stable. By following these tips, you can be sure that your model will perform better even with data you cannot see.
Why should you listen to my advice? I have been working in this field for almost four years, participating in over 80 machine learning competitions and working on several machine learning projects from start to finish. I have also helped many experts build better and more reliable models over the years.
1. Clean the data
Cleaning the data is the most essential part. You need to fill in the missing values, deal with outliers, standardize the data, and ensure its validity. Sometimes, cleaning through a Python script doesn’t really work. You need to analyze each and every sample one by one to make sure there are no issues. I know it will take you a lot of time, but trust me, cleaning the data is the most important part of the machine learning ecosystem.
For example, when I was training an automatic speech recognition model, I found several issues in the dataset that could not be solved by simply removing characters. I had to listen to the audio and rewrite the accurate transcript. There were some transcripts that were quite vague and did not make sense.
2. Add more data
Increasing the data volume can improve model performance. Adding more relevant and diverse data to the training set can help the model learn more patterns and make better predictions. If your model lacks diversity, it may perform well on the majority class, but poorly on the minority class.
Many data scientists are using generative adversarial networks (GANs) to generate more diverse datasets. They do this by training the GAN model on existing data and then using it to generate a synthetic dataset.
3. Feature engineering
Feature engineering involves creating new features from existing data and also removing unnecessary features that contribute less to the model's decision making. This provides the model with more relevant information to make predictions.
You need to perform a SHAP analysis, analyze the feature importance, and determine which features are important to the decision-making process. These can then be used to create new features and remove irrelevant ones from the dataset. This process requires a deep understanding of the business use case and each feature in detail. If you don’t understand the features and their usefulness to the business, you will be walking blindly down the path.
4. Cross validation
Cross-validation is a technique used to evaluate the performance of a model on multiple subsets of data, thereby reducing the risks of overfitting and providing a more reliable estimate of its generalization ability. This will give you insight into whether your model is stable enough or not.
Calculating accuracy across the entire test set may not provide complete information about your model’s performance. For example, the first fifth of the test set might show 100% accuracy, while the second fifth might perform poorly with only 50% accuracy. Despite this, the overall accuracy might still be around 85%. This discrepancy indicates that the model is unstable and requires cleaner, more diverse data to retrain it.
So instead of doing a simple evaluation of the model, I recommend using cross-validation and providing it with several metrics that you want to test the model against.
5. Hyperparameter optimization
Training the model with default parameters may seem simple and fast, but you are missing out on the opportunity to improve performance as in most cases the model is not optimized. To increase the model performance during testing, it is highly recommended to perform thorough hyperparameter optimization in your machine learning algorithms and save those parameters so that you can use them to train or retrain your models next time.
Hyperparameter tuning involves adjusting external settings to optimize model performance. Finding the right balance between overfitting and underfitting is crucial to improve model accuracy and reliability. Sometimes, it can improve model accuracy from 85% to 92%, which is quite significant in the field of machine learning.
6. Experiment with different algorithms
Model selection and experimentation with different algorithms are critical to finding the best fit for the given data. Don’t limit yourself to simple algorithms for tabular data. If your data has multiple features and 10K samples, then you should consider neural networks. Sometimes, even logistic regression can provide amazing results for text classification that cannot be achieved through deep learning models like LSTM.
Start with simple algorithms and then slowly experiment with advanced algorithms to achieve even better performance.
7. Assembly
Ensemble learning involves combining multiple models to improve overall predictive performance. Creating an ensemble of models, each with their own strengths, can lead to more stable and accurate models.
Combining models has often given me better results, sometimes allowing me to achieve a top 10 position in machine learning competitions. Don't discard underperforming models; combine them with a group of high-performing models and your overall accuracy will increase.
Ensembling, dataset cleaning, and feature engineering have been my top three strategies for winning competitions and achieving high performance, even on unseen datasets.
Final Thoughts
There are more tips that only work for certain types of machine learning fields. For example, in computer vision, we should focus on image augmentation, model architecture, preprocessing techniques, and transfer learning. However, the seven tips we discussed above (data cleaning, adding more data, feature engineering, cross-validation, hyperparameter optimization, experimenting with different algorithms, and ensemble) are universally applicable and beneficial for all machine learning models. By implementing these strategies, you can significantly improve the accuracy, reliability, and robustness of your predictive models, leading to better insights and more informed decision-making.
Abid Ali Awan (@1abidaliawan) is a certified data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in technology Management and a Bachelor's degree in Telecommunication Engineering. His vision is to create an ai product using a graph neural network for students struggling with mental illness.
