7 Ways to Improve Your Machine Learning Models

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Are you struggling to improve the model performance during the testing phases? Even if you improve the model, it fails miserably in production for unknown reasons. If you are struggling with similar problems, then you are at the right place. 

In this blog, I will share 7 tips on making your model accurate and stable. By following these tips, you can be sure that your model will perform better even on unseen data. 

Why should you listen to my advice? I have been in this field for almost four years, participating in 80+ machine running competitions and working on several end-to-end machine learning projects. I have also helped many experts build better and more reliable models for years.

1. Clean the Data

Cleaning the data is the most essential part. You need to fill in missing values, deal with outliers, standardize the data, and ensure data validity. Sometimes, cleaning through a Python script doesn’t really work. You have to look at each and every sample one by one to ensure there are no issues. I know it will take a lot of your 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 multiple issues in the dataset that could not be solved by simply removing characters. I had to listen to the audio and rewrite the accurate transcription. There were some transcriptions that were quite vague and did not make sense.

2. Add More Data

Increasing the volume of data can often lead to improved 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 now using Generative Adversarial Networks (GAN) to generate more diverse datasets. They achieve 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 SHAP analysis, look at feature importance analysis, and determine which features are important to the decision-making process. Then, they can be used to create new features and remove irrelevant ones from the dataset. This process requires a thorough understanding of the business use case and each feature in detail. If you don’t understand the features and how they are useful for the business, you will be walking down the road blindly.

4. Cross-Validation

Cross-validation is a technique used to assess a model’s performance across multiple subsets of data, reducing overfitting risks and providing a more reliable estimate of its ability to generalize. This will provide you with the information if your model is stable enough or not. 

Calculating the accuracy on the entire testing set may not provide complete information about your model’s performance. For instance, the first fifth of the testing set might show 100% accuracy, while the second fifth could 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 more clean and diverse data for retraining.

So, instead of performing a simple model evaluation, I recommend using cross-validation and providing it with various metrics you want to test the model on.

5. Hyperparameter Optimization

Training the model with default parameters might seem simple and fast, but you are missing out on improved performance, as in most cases your model is not optimized. To increase the performance of your model during testing, it is highly recommended to thoroughly perform hyperparameter optimization on machine learning algorithms, and save those parameters so that next time you can use them for training or retraining your models.

Hyperparameter tuning involves adjusting external configurations to optimize model performance. Finding the right balance between overfitting and underfitting is crucial for improving the model’s accuracy and reliability. It can sometimes improve the accuracy of the model from 85% to 92%, which is quite significant in the machine learning field.

6. Experiment with Different Algorithms

Model selection and experimenting with various algorithms is crucial to finding the best fit for the given data. Do not restrict yourself to only simple algorithms for tabular data. If your data has multiple features and 10 thousand 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. Ensembling

Ensemble learning involves combining multiple models to improve overall predictive performance. Building an ensemble of models, each with its own strengths, can lead to more stable and accurate models. 

Ensembling the models has often given me improved results, sometimes leading to a top 10 position in machine learning competitions. Don’t discard low-performing models; combine them with a group of high-performing models, and your overall accuracy will increase. 

Ensembling, cleaning the dataset, and feature engineering have been my three best 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 instance, in computer vision, we need to focus on image augmentation, model architecture, preprocessing techniques, and transfer learning. However, the seven tips discussed above—cleaning the data, adding more data, feature engineering, cross-validation, hyperparameter optimization, experimenting with different algorithms, and ensembling—are universally applicable and beneficial for all machine learning models. By implementing these strategies, you can significantly enhance 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 professional who loves building machine learning models. Currently, he is focusing 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 build an AI product using a graph neural network for students struggling with mental illness.