Define model

The training phase of AutoML must be governed by budget constraints and reproducibility. Without fixed data versions and consistent random seeds, results become difficult to compare.

I record the configuration, runtime, best-performing model, and validation metrics for every search iteration. Without experiment tracking, automated results are untraceable.

In the previous article, we discussed the first step of the automated machine learning (AutoML) workflow—data preparation. Ensuring effective utilization of data is critical to successful model deployment. During this stage, we organized and cleaned the data to fully prepare it for subsequent model training. Next, we delve into the model training process—the core of AutoML.

Overview of Model Training

The goal of model training is to generate a new predictive model using cleaned and prepared data and machine learning algorithms. This step involves selecting appropriate algorithms, configuring hyperparameters, and executing the actual training procedure.

When performing model training with AutoML, first confirm the candidate algorithms, feature preprocessing steps, time budget, evaluation metrics, and validation set. Even automated search requires clearly defined boundaries.

Algorithm Selection

In AutoML, algorithm selection is typically automated. The system evaluates multiple algorithms and selects the one best suited to the data’s characteristics. Common machine learning algorithms include:

• Decision Tree
• Random Forest
• Support Vector Machine (SVM)
• Neural Network
• Gradient Boosting Machine (GBM)

For example, in a house price prediction project, an AutoML system might initially try Random Forest and Gradient Boosting Tree algorithms, as they often perform well on structured tabular data.

Hyperparameter Tuning

Hyperparameters are key settings that govern model behavior and performance—and are typically specified before training begins. In AutoML workflows, common hyperparameter tuning techniques include:

• Grid Search
• Random Search
• Bayesian Optimization

For Random Forest, examples of hyperparameters to tune include:

• n_estimators (number of trees)
• max_depth (maximum depth of each tree)
• min_samples_split (minimum number of samples required to split an internal node)

Here's an example using Grid Search to find optimal settings:

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import GridSearchCV

# Define model
rf = RandomForestRegressor()

# Define hyperparameter grid
param_grid = {
    'n_estimators': [100, 200],
    'max_depth': [None, 10, 20, 30],
    'min_samples_split': [2, 5, 10]
}

# Perform grid search
grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=3)
grid_search.fit(X_train, y_train)

# Output best parameters
print("Best parameters:", grid_search.best_params_)

Model Training

Once suitable algorithms and optimal hyperparameters have been identified, the next step is actual model training. During training, the model learns patterns from the data and updates its internal parameters to improve prediction accuracy.

# Train final model using optimized hyperparameters
best_rf = grid_search.best_estimator_
best_rf.fit(X_train, y_train)

Here, we use the best estimator returned by GridSearchCV for final training. This yields an optimized model better fitted to our training data.

Training Evaluation

Although model training is essential, we must ensure the trained model generalizes well to unseen data. To assess effectiveness, we commonly apply cross-validation after training to evaluate model stability. We’ll explore model evaluation in depth in the next chapter.

If you haven’t fully internalized “AutoML Workflow: Model Training”, revisit the four actions outlined on this card to walk through the process again.

When reviewing “AutoML Workflow: Model Training”, avoid jumping straight into large-scale projects. Instead, start with a simple, minimal example to verify whether the core workflow is clear.

Summary

In this chapter, we thoroughly examined the model training phase of the AutoML workflow—from algorithm selection and hyperparameter tuning to actual model fitting. Every step aims to maximize predictive performance. Using high-quality inputs and sound training strategies at each stage is paramount.

Content like “AutoML Workflow: Model Training” can easily distract readers with implementation details. First, grasp the main flow depicted in the diagram; then return to the text to verify the environment, inputs, outputs, and decision criteria.

The next article will cover model evaluation, ensuring that our trained models perform robustly on unseen data. We’ll discuss practical methods for validating model performance and how to leverage evaluation metrics to guide real-world decisions.