Begin with a brief overview of ensemble learning and its importance. Mention the learning objectives and activities
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Question:
Begin with a brief overview of ensemble learning and its importance.
Mention the learning objectives and activities for the lab.
Ensemble Techniques:
Bagging:
Load a sample dataset into KNIME.
Use the "Bagging Learner" node found in the "Ensemble Learning" category to implement a bagging algorithm. Configure parameters like the base learner and the number of iterations.
Evaluate the performance of the bagging model.
Boosting:
Load another sample dataset.
Utilize the "Boosting Learner" node also in the "Ensemble Learning" category to apply a boosting algorithm like AdaBoost. Configure the base learner and other relevant parameters.
Evaluate the boosting model's performance.
Stacking:
Load a third dataset or use an existing one.
Combine multiple base models using the "Stacked Ensembles Learner" node found in the "Ensemble Learning" category
Configure the base learners and meta learner.
Assess the performance of the stacked model.
BiasVariance Tradeoff:
Create a synthetic dataset with controlled bias and variance.
Train models with varying complexity eg a simple linear model and a complex polynomial model
Visualize the tradeoff between bias and variance using scatter plots and learning curves.
Overfitting vs Underfitting:
Use a dataset that exhibits clear signs of overfitting and underfitting.
Train models with different levels of complexity eg linear, polynomial of varying degrees
Compare training and validation performance to identify overfitting and underfitting.
Class Imbalance:
Import a dataset with class imbalance.
Apply undersampling techniques eg "Row Sampling" node and compare results.
Apply oversampling techniques eg "SMOTE" node and observe changes in model performance.
Implement hybrid approaches if necessary eg combining oversampling and undersampling techniques
Interpretability in Machine Learning:
Load a dataset suitable for classification or regression.
Train an interpretable model like a decision tree or a linear regression model.
Use nodes like "Decision Tree" or "Rule Engine" to extract rules or explanations.
Apply LIME or SHAP values using appropriate KNIME extensions.
WrapUp and Discussion:
Summarize the key points covered in the lab.
Engage students in a discussion about potential applications and challenges related to the topics covered.
Related Book For
Auditing Cases An Interactive Learning Approach
ISBN: 9780134421827
7th Edition
Authors: Mark S Beasley, Frank A. Buckless, Steven M. Glover, Douglas F Prawitt
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