In industrial visual inspection, decision tree-based models are fundamental tools. Random Forest combines independently trained trees in parallel and averages their results, while GBM builds trees sequentially to correct residual errors, achieving higher precision. Understanding GBM is key to later exploring its evolutions like XGBoost and LightGBM.
GBM builds a strong model by summing many simple trees trained one after another. The first tree makes an initial prediction that likely contains errors. For each data point, we calculate the residual—the difference between the prediction and the true value. The next tree is trained to predict these residuals, correcting the previous tree's errors. Instead of applying the full correction at once, we multiply it by a learning rate, a small value (0.01 to 0.1) that controls how much we adjust in each iteration, helping to avoid overfitting. The final prediction is the sum of all trees' predictions weighted by the learning rate.
Key hyperparameters include learning_rate, max_depth (maximum depth of each tree), n_estimators (number of trees), and min_samples_leaf (minimum samples per leaf), which control model complexity and generalization.
In plant environments, GBM can overfit if hyperparameters are not well regulated, especially with noisy or imbalanced data. Training can be slow with large data volumes, and model interpretability is challenging, complicating the explanation of decisions in production.
To implement GBM in visual inspection, it is essential to tune key hyperparameters: learning_rate to control learning speed, n_estimators to define how many trees to sum, max_depth to limit tree complexity, and min_samples_leaf to avoid leaves with too few samples that can lead to overfitting. Starting with a low learning_rate helps avoid abrupt adjustments, increasing the number of trees gradually. Techniques like Grid Search or Random Search facilitate finding the best combination. Additionally, validating the model with real plant data and ensuring good data quality and balance is crucial to prevent bias.
With this clear foundation, the next step will be to explore how XGBoost improves GBM with computational and regularization optimizations.