Decision Tree vs Neural Network Short-Term Regression Models Part 2 of 3.

Data as of 10 Jan 25

Different forms of Decision Tree models Explained:

Ensemble Models: Ensemble models in machine learning are like having a team of experts working together to make better predictions than any single expert could achieve alone. Here's a simple explanation:

1. Combining Predictions: Ensemble models use multiple individual models (like decision trees or neural networks) to make predictions and then combine those predictions, often through methods like averaging or majority voting.

2. Why They Work:

   • Reduce Variance and Bias: By combining multiple models, ensemble methods reduce the impact of variance (sensitivity to specific data subsets) and bias (simplification errors), leading to more robust predictions.

   • Capture Complex Patterns: Different models can capture different patterns in the data, and their combination can provide a more comprehensive understanding.

3. Single Decision Tree:

   • A single decision tree is a basic machine learning model that makes predictions by recursively splitting the data based on feature values. It starts from the root and splits the data into subsets, continuing this process until it reaches the leaf nodes, which provide the final prediction. While simple, single trees can be prone to overfitting, especially with complex datasets.

4. Boosted Ensemble of Trees:

   • Boosted ensembles, such as AdaBoost, Gradient Boosting, or XGBoost, involve training multiple decision trees sequentially. Each subsequent tree aims to correct the errors made by the previous one. This sequential correction helps in reducing bias, but it requires careful tuning to prevent overfitting.

5. Bagging Ensemble of Trees:

   • Bagging, or Bootstrap Aggregating, involves training multiple decision trees in parallel on different bootstrapped samples of the dataset. The final prediction is obtained by averaging (for regression) or voting (for classification) the predictions of all the trees. This method reduces variance and overfitting.

6. Random Forests:

   • Random Forests are a specific type of bagging ensemble. In addition to training trees on different bootstrapped datasets, each tree considers only a random subset of features at each split. This introduces diversity among the trees, enhancing the model's generalization