Week_6_Assignment_ITCS_6500
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University of North Carolina, Charlotte *
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6500
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Industrial Engineering
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Feb 20, 2024
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docx
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Uploaded by arkasuper
Explain Ensemble Learning.
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Ensemble learning is a machine learning technique that involves combining multiple models
to achieve better predictive performance than any single model can achieve on its own. It leverages the collective wisdom of multiple models to overcome their individual limitations and biases.
Here's a breakdown of the key concepts:
How it works:
1.
Train multiple models:
Different models (e.g., decision trees, support vector machines) are trained on the same dataset, potentially with variations in their algorithms, hyperparameters, or training subsets.
2.
Combine predictions:
The predictions from each individual model are combined using various methods like averaging, voting, or stacking.
3.
Improved performance:
The final prediction from the ensemble often outperforms the individual models, leading to higher accuracy, robustness, and generalization.
Types of ensemble methods:
Bagging (Bootstrap aggregating):
Creates multiple models by training each on a different random sample with replacement from the original data. Predictions are averaged for final output.
Boosting:
Trains models sequentially, where each model focuses on learning from the errors of the previous model. Predictions are weighted and combined for an improved outcome.
Stacking:
Trains a meta-model on top of the predictions from several base models, essentially learning how to combine them effectively.
Benefits of ensemble learning:
Improved accuracy:
Ensembles often achieve higher accuracy than individual models due to reduced variance and bias.
Robustness to noise and outliers:
Ensembles are less susceptible to the influence of individual data points, leading to more robust predictions.
Reduced overfitting:
By combining diverse models, ensembles can capture different aspects of the data and avoid overfitting to specific patterns.
Challenges of ensemble learning:
Increased complexity:
Training and managing multiple models can be computationally expensive and time-consuming.
Interpretability:
Understanding how ensembles make predictions can be more challenging than with individual models.
Choosing the right models and methods:
Selecting the best combination of models and ensemble methods requires careful evaluation and experimentation.
Overall, ensemble learning is a powerful technique for enhancing the performance and robustness of machine learning models. It's widely used in various domains like image recognition, natural language processing, and forecasting.
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