Standalone Model Challenges

Before appreciating why ensembles are useful, it helps to understand the common challenges that standalone models - single models trained in isolation - tend to run into.

High Variance

Some models, like deep Decision Trees, are highly sensitive to the specific training data they see - small changes in the data can lead to very different predictions.

High Bias

Other models, like simple Linear or Logistic Regression, may be too rigid to capture the true patterns in complex data, consistently under-fitting the problem.

Overfitting

# A standalone model may perform very well on training data
# but poorly on unseen test data - it has memorized noise
# instead of learning generalizable patterns.

Instability

A single model's prediction can be thrown off by outliers, noisy features, or an unlucky train/test split, since there's no second opinion to balance it out.

Ensemble methods address these challenges directly - averaging out variance, combining different biases, and reducing the impact of any single model's mistakes.

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