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Machine learning (ML) algorithms are key to building intelligent models that learn from data to solve a particular task, namely making predictions, classifications, detecting anomalies, and more. Optimizing ML models entails adjusting the data and the algorithms that lead to building such models, to achieve more accurate and efficient results, and improving their performance against new or unexpected situations.
The below list encapsulates the five key tips for optimizing the performance of ML algorithms, more specifically, optimizing the accuracy or predictive power of the resulting ML models built. Let’s have a look.
1. Preparing and Selecting the Right Data
Before training an ML model, it is very important to preprocess the data used to train it: clean the data, remove outliers, deal with missing values, and scale numerical variables when needed. These steps often help enhance the quality of the data, and high-quality data is often synonymous with high-quality ML models trained upon them.
Besides, not all the features in your data might be relevant to the model built. Feature selection techniques help identify the most relevant attributes that will influence the model results. Using only those relevant features may help not only reduce your model’s complexity but also improve its performance.
2. Hyperparameter Tuning
Unlike ML model parameters which are learned during the training process, hyperparameters are settings selected by us before training the model, just like buttons or gears in a control panel that may be manually adjusted. Adequately tuning hyperparameters by finding a configuration that maximizes the model performance on test data can significantly impact the model performance: try experimenting with different combinations to find an optimal setting.
3. Cross-Validation
Implementing cross-validation is a clever way to increase your ML models’ robustness and ability to generalize to new unseen data once it is deployed for real-world use. Cross-validation consists of partitioning the data into multiple subsets or folds and using different training/testing combinations upon those folds to test the model under different circumstances and consequently get a more reliable picture of its performance. It also reduces the risks of overfitting, a common problem in ML whereby your model has “memorized” the training data rather than learning from it, hence it struggles to generalize when it is exposed to new data that looks even slightly different than the instances it memorized.
4. Regularization Techniques
Continuing with the overfitting problem sometimes is caused by having built an exceedingly complex ML model. Decision tree models are a clear example where this phenomenon is easy to spot: an overgrown decision tree with tens of depth levels might be more prone to overfitting than a simpler tree with a smaller depth.
Regularization is a very common strategy to overcome the overfitting problem and thus make your ML models more generalizable to any real data. It adapts the training algorithm itself by adjusting the loss function used to learn from errors during training, so that “simpler routes” towards the final trained model are encouraged, and “more sophisticated” ones are penalized.
5. Ensemble Methods
Unity makes strength: this historical motto is the principle behind ensemble techniques, consisting of combining multiple ML models through strategies such as bagging, boosting, or stacking, capable of significantly boosting your solutions’ performance compared to that of a single model. Random Forests and XGBoost are common ensemble-based techniques known to perform comparably to deep learning models for many predictive problems. By leveraging the strengths of individual models, ensembles can be the key to building a more accurate and robust predictive system.
Conclusion
Optimizing ML algorithms is perhaps the most important step in building accurate and efficient models. By focusing on data preparation, hyperparameter tuning, cross-validation, regularization, and ensemble methods, data scientists can significantly enhance their models’ performance and generalizability. Give these techniques a try, not only to improve predictive power but also help create more robust solutions capable of handling real-world challenges.
Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.
