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Uncertainty in Multiclass Classification

Dr. Rishabh Pathak
1. What is Uncertainty in Classification? Uncertainty refers to the model’s confidence or doubt in its predictions. Quantifying uncertainty is important to understand how reliable each prediction is. In multiclass classification , uncertainty estimates provide probabilities over multiple classes, reflecting how sure the model is about each possible class. 2. Methods to Estimate Uncertainty in Multiclass Classification Most multiclass classifiers provide methods such as: predict_proba: Returns a probability distribution across all classes. decision_function: Returns scores or margins for each class (sometimes called raw or uncalibrated confidence scores). The probability distribution from predict_proba captures the uncertainty by assigning a probability to each class. 3. Shape and Interpretation of predict_proba in Multiclass Output shape: (n_samples, n_classes) Each row corresponds to the probabilities of ...
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Ensembles of Decision Trees

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Dr. Rishabh Pathak

1. What are Ensembles?

  • Ensemble methods combine multiple machine learning models to create more powerful and robust models.
  • By aggregating the predictions of many models, ensembles typically achieve better generalization performance than any single model.
  • In the context of decision trees, ensembles combine multiple trees to overcome limitations of single trees such as overfitting and instability.

2. Why Ensemble Decision Trees?

Single decision trees:

  • Are easy to interpret but tend to overfit training data, leading to poor generalization,.
  • Can be unstable because small variations in data can change the structure of the tree significantly.

Ensemble methods exploit the idea that many weak learners (trees that individually overfit or only capture partial patterns) can be combined to form a strong learner by reducing variance and sometimes bias.

3. Two Main Types of Tree Ensembles

(a) Random Forests

  • Random forests are ensembles consisting of many decision trees.
  • Each tree is built on a bootstrap sample of the training data (sampling with replacement).
  • At each split in a tree, only a random subset of features is considered for splitting.
  • The aggregated prediction over all trees (majority vote for classification, average for regression) reduces overfitting by averaging diverse trees.

Key details:

  • Randomness ensures the trees differ; otherwise, correlated trees wouldn't reduce variance.
  • Trees grown are typically deeper than single decision trees because the random feature selection introduces diversity.
  • Random forests are powerful out-of-the-box models requiring minimal parameter tuning and usually do not require feature scaling.

(b) Gradient Boosted Decision Trees

  • Build trees sequentially, where each new tree tries to correct errors of the combined ensemble built so far.
  • Unlike random forests which average predictions, gradient boosting fits trees to the gradient of a loss function to gradually improve predictiveness.
  • This process often yields higher accuracy than random forests but training is more computationally intensive and sensitive to overfitting.

4. How Random Forests Inject Randomness

  • Data Sampling: Bootstrap sampling ensures each tree is trained on a different subset of data.
  • Feature Sampling: Each split considers only a subset of features randomly selected.

These two layers of randomness ensure:

  • Individual trees are less correlated.
  • Averaging predictions reduces variance and prevents overfitting seen in single deep trees.

5. Strengths of Ensembles of Trees

  • Robustness and accuracy: Reduced overfitting due to averaging or boosting.
  • Minimal assumptions: Like single trees, ensembles typically do not require feature scaling or extensive preprocessing.
  • Handle large feature spaces and data: Random forests can parallelize tree building and scale well.
  • Feature importance: Ensembles can provide measures of feature importance from aggregated trees.

6. Weaknesses and Considerations

  • Interpretability: Ensembles lose the straightforward interpretability of single trees. Hundreds of trees are hard to visualize and explain.
  • Computational cost: Training a large number of trees, especially with gradient boosting, can be time-consuming.
  • Parameter tuning: Gradient boosting requires careful tuning (learning rate, tree depth, number of trees) to avoid overfitting.

7. Summary Table for Random Forests and Gradient Boosting

        Feature

            Random       Forests

Gradient Boosted Trees

Tree construction

Parallel, independent bootstrap samples

Sequential, residual fitting

Randomness

Data + feature sampling

Deterministic, based on gradients

Overfitting control

Averaging many decorrelated trees

Regularization, early stopping, shrinkage

Interpretability

Lower than single trees but feature importance available

Lower; complex, but feature importance measurable

Computation

Parallelizable; faster

Slower; sequential

Typical use cases

General-purpose, robust models

Performance-critical tasks, often winning in competitions

8. Additional Notes

  • Both methods build on the decision tree structure explained in detail,.
  • Random forests are often preferred as a baseline for structured data due to simplicity and effectiveness.
  • Gradient boosted trees can outperform random forests when carefully tuned but are less forgiving.

 

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