That’s a good question. Ensemble methods like bagging and random forest are powerful tools in machine learning, but they are not always the best choice depending on the specifics of the problem at hand. Here are a few reasons why you might not have seen impressive results in your professor’s demonstration:

  1. Data characteristics: The performance of machine learning algorithms can significantly depend on the specific characteristics of the data they are applied to. For instance, if your data is linearly separable, then a simple logistic regression might perform quite well, while more complex models could overfit and perform worse. Similarly, a single decision tree could outperform a random forest if the data is simple enough that a single tree can capture the underlying patterns.
  2. Hyperparameters tuning: Random forest and bagging have a number of hyperparameters that need to be tuned (like the number of estimators, maximum depth of trees, etc.). If these hyperparameters aren’t set appropriately, the model’s performance can be suboptimal. For example, if you use too many trees in a random forest, it might take a lot of computation time without necessarily improving accuracy much.
  3. Bias-variance tradeoff: Bagging and random forest are techniques that can help reduce variance in the model, but they might increase bias. If your dataset is small, the tradeoff may lean towards having more variance (overfitting) rather than more bias (underfitting). This might lead to ensemble models performing worse than a single decision tree.
  4. Noise and outliers: Bagging and random forest models can be sensitive to noise and outliers. If the data is not properly cleaned and preprocessed, these models may not perform well.

That being said, there are several reasons why ensemble methods like bagging and random forest are generally regarded as impressive and powerful:

  1. Reducing overfitting: Bagging and random forest can reduce overfitting by averaging out the results from multiple models. This generally makes the final model more stable and less sensitive to slight changes in the training data.
  2. Handling high dimensionality: Random forests, in particular, are great for datasets with a large number of features because they randomly select subsets of features for each tree. This helps manage the “curse of dimensionality” and allows these models to handle high-dimensional data more effectively than many other models.
  3. Flexibility: These models can handle both regression and classification tasks, and they can work with both numerical and categorical data. This makes them quite flexible for different types of problems.
  4. Model interpretability: While individual trees in a random forest may not be interpretable, the importance of different features in the model can be quantified, providing insights into the model.

So while your professor’s demonstration may not have shown the most impressive results for these techniques, remember that there are no one-size-fits-all solutions in machine learning. Different tools are useful for different tasks, and ensemble methods like bagging and random forest can be extremely effective tools in the right circumstances.