Your Machine Learning Models Can Be Improved in 7 Ways
Introduction#
During the testing stages, are you having trouble getting the model to perform better? For whatever reason, the model performs horribly in production, no matter how much you tweak it. You're in the right place if you're having issues similar to these.
This blog offers seven suggestions for improving the accuracy and stability of your model. You may be certain that your model will perform better even on unseen data if you adhere to these suggestions.
1. Data Cleaning#
The most crucial step is to clean the data. Missing values must be filled in, outliers must be dealt with, data must be standardized, and data validity must be guaranteed. Occasionally, using a Python script to clean doesn't actually work. To make sure there are no problems, you need to examine each sample individually. Although it will require a significant amount of your time, I assure you that data cleansing is the most crucial component of the machine learning ecosystem.
2. Include More Data#
A larger data set frequently results in better model performance. The model can learn more patterns and improve its prediction skills by including more diverse and pertinent data in the training set. Your model might perform well in the majority class but poorly in the minority class if it isn't diverse.
Generative Adversarial Networks (GAN) are being used by many data scientists to create more diversified datasets. They accomplish this by utilizing the GAN model to create a synthetic dataset after training it on real data.
3. Engineering features#
In feature engineering, extraneous features that don't add anything to the model's decision-making process are eliminated and new features are created from the data that already exists. This gives the model more pertinent data with which to forecast.
Determine which aspects are crucial to the decision-making process by doing a feature importance analysis and a SHAP analysis. Subsequently, they can be employed to generate novel characteristics and eliminate superfluous ones from the dataset. A detailed grasp of each feature and the business use case is necessary for this procedure. You will be going down the road blindly if you don't know the characteristics and how they are beneficial to the company.
4. Comparative Evaluation#
By evaluating a model's performance over several data subsets, a process known as cross-validation can lower the likelihood of overfitting and produce a more accurate estimate of the model's capacity for generalization. This will tell you whether or not your model is sufficiently stable.
Finding the accuracy over the whole testing set might not provide you all the details you need to understand how well your model is performing. As an example, the testing set's first fifth may exhibit 100% accuracy, whereas the second fifth may do poorly, displaying only 50% accuracy. The overall accuracy may still be approximately 85% in spite of this. This disparity suggests that the model needs more clean, varied data for retraining because it is unstable.
Therefore, I suggest utilizing cross-validation and feeding it with the several metrics you wish to evaluate the model on, as opposed to carrying out a straightforward model evaluation.
5. Optimization of Hyperparameters#
Although training the model with the default settings may appear quick and easy, you are usually not getting the best performance out of your model. It is strongly advised that you undertake extensive hyperparameter optimization on machine learning algorithms in order to improve your model's performance during testing. You should then preserve those parameters so that you can use them for training or retraining your models in the future.
To maximize model performance, external configurations are adjusted through hyperparameter tuning. Enhancing the accuracy and dependability of the model requires striking the correct balance between overfitting and underfitting. In the realm of machine learning, it can occasionally increase the model's accuracy from 85% to 92%, which is highly important.
6. Play Around with Various Algorithms#
To determine which model best fits the provided data, selecting a model and experimenting with different techniques are essential. Don't limit yourself to using tabular data and simple algorithms alone. Consider neural networks if your data contains 10,000 samples and several features. Even logistic regression can occasionally produce remarkable text classification outcomes that deep learning models like LSTM are unable to match.
To get even higher performance, start with basic algorithms and gradually experiment with more complex ones.
7. Comparing#
Several models are combined in ensemble learning to enhance overall predicting performance. More precise and reliable models can be created by assembling a group of models, each with unique strengths.
We can notice the performance has increased significantly after assembling the models. Your overall accuracy will rise if you merge underperforming models with a set of performing models instead of discarding the underperforming ones.
Even on unseen datasets, the three best methods for excelling in competitions and attaining high performance have been assembling, feature engineering, and dataset cleaning.
Conclusion#
There are other hints that are specific to particular domains of machine learning. For example, in computer vision, we must prioritize model design, preprocessing methods, transfer learning, and picture augmentation. Nevertheless, the seven recommendations covered above are helpful and broadly relevant for all machine learning models. You may greatly improve your predictive models' accuracy, dependability, and robustness by putting these tactics into practice. This will provide you with greater insights and help you make better decisions.