How to Excel Ensemble Learning for Creating Accurate Machine Learning Assignment
When developing predictive machine learning models, students and professionals alike often explore several strategies to improve model accuracy. These include experimenting with different algorithms, fine-tuning hyperparameters, crafting better features, or optimizing how data is split and processed. One particularly effective strategy is ensemble learning—a method that combines the strengths of multiple models to create a single, more accurate and stable predictive model. Rather than relying on just one algorithm, ensemble learning leverages a group of weak learners, improving overall performance and reducing the risk of overfitting or bias.
This guide focuses on using ensemble learning in MATLAB, particularly for solving regression problems. The same techniques can be adapted for classification tasks with minor adjustments. Whether you’re exploring ensemble models as part of your coursework or seeking help with machine learning assignment, this tutorial offers a practical approach. You’ll learn how to clean and prepare real-world data, create ensemble models using MATLAB’s built-in functions and learner templates, evaluate model accuracy through cross-validation or test sets, and refine performance through iterative improvements. MATLAB provides a robust environment for exploring these concepts, making it an ideal tool for both learning and applying advanced machine learning techniques effectively.
What is Ensemble Learning?
Ensemble learning refers to the process of combining the predictions of several base models—often referred to as weak learners—to generate a more robust model. Weak learners may struggle with accuracy individually but can complement each other when grouped. By aggregating their outputs, the ensemble can generalize better and reduce variance and bias in predictions.
This approach is effective across a variety of machine learning and deep learning problems. In this guide, we’ll focus on creating an ensemble for a regression problem using MATLAB’s machine learning capabilities.
Step 1: Preparing the Data
Let’s consider a dataset about cars with various features like horsepower, weight, and acceleration. Our target is to predict Miles Per Gallon (MPG).
load carbigcarTable = table(Acceleration, Cylinders, Displacement, Horsepower, Model_Year, Weight, MPG);head(carTable)
The dataset includes rows with missing values. Identifying and managing these missing entries is crucial for training an accurate model.
missingElements = ismissing(carTable);rowsWithMissingValues = any(missingElements, 2);missingValuesTable = carTable(rowsWithMissingValues, :)
In our example, 14 rows contain missing data, 8 of which are missing the target variable ‘MPG’. These rows are removed, as they do not contribute to supervised learning:
rowsMissingMPG = ismissing(carTable.MPG);carTable(rowsMissingMPG, :) = [];
Next, we split the cleaned dataset into training and testing subsets. Typically, 70% of the data is used for training and 30% for testing:
numRows = size(carTable,1);[trainInd, ~, testInd] = dividerand(numRows, 0.7, 0, 0.3);trainingData = carTable(trainInd, :);testingData = carTable(testInd, :);
Step 2: Creating the Ensemble Model
Creating the ensemble model is a crucial step in building a high-performing machine learning system. In this stage, multiple weak learners are combined to form a single, more accurate model. MATLAB provides powerful functions like fitrensemble for regression and fitcensemble for classification, which make it easy to create ensemble models using just a few lines of code. You can start with default settings or customize various parameters such as the number of learners, the type of aggregation method, and the structure of individual learners. Whether you're using bagging, boosting, or other techniques, this step sets the foundation for improved accuracy and generalization. This part of the workflow is not only essential for real-world applications but also extremely useful.
Using Built-In Algorithms
MATLAB provides a direct way to create ensemble models using the fitrensemble function for regression.
Mdl = fitrensemble(trainingData, 'MPG');
This command trains an ensemble of 100 regression trees using the LSBoost algorithm by default. To improve results or tailor the model to your dataset’s characteristics, you can change the aggregation method and number of learners.
Let’s modify the method to Bagging (which often performs better for smaller datasets) and reduce the number of learning cycles to 30:
Mdl = fitrensemble(trainingData, 'MPG', 'Method', 'Bag', 'NumLearningCycles', 30);
Using Learner Templates
Sometimes, customizing the individual learners can lead to better results—especially when dealing with missing data. Using trees with surrogate splits is one such method. This is done by creating a learner template:
templ = templateTree('Surrogate','all', 'Type', 'regression');templMdl = fitrensemble(trainingData, 'MPG', 'Method', 'Bag', ... 'NumLearningCycles', 30, 'Learners', templ);
Templates give greater control over the weak learners, such as specifying tree depth, split criteria, and handling of missing values.
Step 3: Evaluating the Ensemble
Training the ensemble is only part of the process. Evaluating its performance ensures it can generalize well to unseen data.
Cross-Validation Evaluation
For smaller datasets, k-fold cross-validation is a preferred evaluation method. It partitions the data into folds and rotates the training/testing sets across them:
cvens = crossval(templMdl); kfoldLoss(cvens)
This returns the average mean squared error (MSE) across the folds. You can further visualize how performance evolves as more learners are added:
cValLoss = kfoldLoss(cvens, 'mode', 'cumulative');plot(cValLoss, 'r--');xlabel('Number of Trees');ylabel('Cross-Validation Loss');
Test Set Evaluation
If you have enough data, you can evaluate the ensemble using a hold-out test set:
loss(templMdl, testingData, 'MPG')
This calculates the prediction error on the unseen data. You can also visualize cumulative test loss:
plot(loss(templMdl, testingData, 'MPG', 'mode', 'cumulative'));xlabel('Number of Trees');ylabel('Test Loss');
Prediction Visualization
Comparing actual vs. predicted values helps spot overfitting or systematic errors:
predMPG = predict(templMdl, testingData);expectMPG = testingData.MPG;plot(expectMPG, expectMPG);hold on;scatter(expectMPG, predMPG);xlabel('True MPG');ylabel('Predicted MPG');hold off;
This plot visually demonstrates how close the predicted values are to actual values.
Step 4: Iterating and Improving the Model
Improvement comes through experimentation. Here are some tips to refine your ensemble model:
Add More Learners
If the cumulative loss keeps dropping as more learners are added, the model could benefit from more cycles. You can add learners using the resume method:
templMdl = resume(templMdl, additionalCycles);
Optimize Hyperparameters Automatically
MATLAB offers built-in support for hyperparameter optimization during training:
Mdl = fitrensemble(trainingData, 'MPG', ... 'Method', 'Bag', ... 'OptimizeHyperparameters', 'auto', ... 'HyperparameterOptimizationOptions', struct('MaxObjectiveEvaluations', 30));
This automatically searches for the best combination of parameters like the number of trees, learning rate, and tree depth.
Experiment with Different Learner Types
Don’t limit yourself to trees. Depending on the problem, other learners like KNN or SVM can be better base models. You can create templates using:
templateKNN(), templateSVM(), templateTree()
These can then be passed to fitrensemble or fitcensemble.
Rethink Data Splitting and Cleaning
Always reassess your data handling steps. You might want to try different ratios for train-test splits or more advanced cleaning methods. The quality of your input data has a direct impact on the model's accuracy.
Final Thoughts
Ensemble learning is one of the most effective techniques in machine learning to improve the accuracy of predictive models. It works by combining the output of multiple weak learners to create a stronger, more reliable model. This approach is particularly valuable when individual models perform inconsistently or struggle to generalize well.
In MATLAB, ensemble learning becomes even more powerful with built-in functions like fitrensemble, templateTree, and various options for validation and optimization. These tools make it easier to build, test, and improve models without excessive manual tuning.
For students looking to solve their MATLAB assignment with high accuracy, applying ensemble learning can be a game-changer. Whether you're dealing with regression or classification problems, creating an ensemble helps reduce bias and variance, leading to more dependable results. MATLAB’s flexible framework allows you to explore different aggregation methods, tune hyperparameters, and customize learner templates to suit your dataset. You can evaluate models using cross-validation or independent test sets, helping you understand performance in real-world scenarios. So, if your goal is to solve your MATLAB assignment using best practices in machine learning, integrating ensemble learning is a smart and efficient approach that delivers results both in academic tasks and practical applications.