Choosing an ML Algorithm (Gemini)
See: ML Algorithms
This section below does not discuss individual ML algorithms. It introduces at a high-level an example workflow.
Ensemble techniques in machine learning combine predictions from multiple individual models called base learners to create a single, robust, and accurate predictive model.
By aggregating predictions, the models reduce errors, prevent overfitting, and improve generalization to new data.
The combined predictions are usually aggregated using averaging (for regression), majority voting (for classification), or another aggregation technique.
Ensemble Learning Workflow
1. Baseline Strategy
The ensemble workflow rarely starts with a complex Machine Learning (ML) algorithm. The workflow often starts with a simple, standard baseline.
The benchmark might start with Logistic Regression for classification or Linear Regression for regression.
This establishes a floor for performance. If a complex, computationally expensive algorithm can't beat the simple baseline, it isn't worth using.
2. Parallel Experimentation
Instead of working with one algorithm after another, use a suite of diverse algorithms using frameworks such as scikit-learn or Automated Machine Learning (AutoML) tools.
models = [
('Logistic Regression', LogisticRegression()),
('Random Forest', RandomForestClassifier()),
('Gradient Boosting', GradientBoostingClassifier()),
('SVM', SVC())
]
Run a script that can evaluate 5 to 10 different types of algorithms (linear, tree-based, distance-based) in a single pass to see which algorithms fit the structure of the data.
By default, Python's scikit-learn and similar machine learning frameworks process these models sequentially.
They will train and evaluate one model at a time first Logistic Regression, then Random Forest, and so on.
Because each model is independent, you can manually speed up this process by running them in parallel.
If you want to speed up things like cross-validation or hyperparameter tuning for each individual model, you can use built-in parallel processing.
Most estimators and validation tools (like GridSearchCV) have an n_jobs parameter. Setting n_jobs=-1 tells your system to use all available CPU cores.
3. Shortlisting and Hyperparameter Tuning
Once the initial parallel run is complete, you have narrowed the field down to the top 2 or 3 performing models.
At this point, you can focus closely on individual algorithms, by fine-tuning them.
Use techniques like Grid Search or Random Search to test hundreds of different internal settings (hyperparameters) for those specific algorithms to squeeze out the best performance.
4. The Ensemble Approach Combining results.
The final solution is rarely just one algorithm. The best results usually come from Ensemble Methods, where multiple algorithms are trained on the data and their predictions are combined.
Voting/Stacking: You might take the top-performing Random Forest, an XGBoost model, and a Neural Network, and let them "vote" on the final output.
A parallel run is a competition to find the best individual model, while an ensemble approach is a collaboration where multiple models work together to get a better result.