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Frequently Asked Questions
Q. 1) For a 2D hyperplane defined by the parameters w=(3,4)w = (3,4)w=(3,4) and w0=−7w_0 = -7w0=−7, compute the absolute value of the geometric margin for the point x=(2,5)x = (2,5)x=(2,5). Round your answer to two decimal places.
The geometric margin is 3.80.
Q. 2) Use a synthetic 2D regression dataset to train an Extra Trees Regressor. Compare its performance with a standard Random Forest model. Analyze the effect of using completely random splits on the predictions and feature importance.
Train the Models: Use synthetic 2D regression data, such as ???? = 5 ???? 1 2 + 3 ???? 2 + noise y=5x 1 2 +3x 2 +noise, to train both models. Extra Trees Regressor uses completely random splits, while Random Forest uses splits that minimize variance. Performance Comparison: Extra Trees may perform slightly worse on noisy data due to random splits, as it sacrifices variance reduction for speed. Random Forest typically shows better performance with more reliable feature importance rankings. Effect of Random Splits: Extra Trees produces less biased feature importance values but may result in less stable predictions due to randomization.
Q. 3) Generate a dataset where the target variable depends on the interaction between two features. Train a decision tree and random forest model. Discuss how tree-based models naturally capture feature interactions compared to linear models.
Generate Data: ???? = ???? 1 × ???? 2 + noise y=x 1 ×x 2 +noise. Train a Decision Tree and Random Forest. Tree-Based Models: Automatically capture interactions (e.g., splits based on ???? 1 x 1 and ???? 2 x 2 separately or jointly). Linear models struggle with interactions unless explicitly modeled (e.g., polynomial terms).
Q. 4) Using a 2D dataset with moderate noise: Train a bagging regressor and a gradient-boosting regressor. Compare their performance on the test set. Discuss the strengths and weaknesses of bagging and boosting for regression tasks.
Bagging (e.g., Random Forest): Reduces variance. Works well on high-variance datasets but struggles with underfitting. Boosting (e.g., Gradient Boosting): Reduces bias. Can overfit noisy data but performs well on moderately noisy datasets. Performance Comparison: Bagging is robust to noise. Boosting captures subtle patterns but may overfit noisy data.
Q. 5) Train a Decision Tree Regressor on a 2D dataset. Visualize the decision boundaries created by the tree. Explain how the tree splits the feature space to make predictions.
The tree splits the feature space into axis-aligned regions. Predictions are constant within each region.
Q. 6) Generate synthetic 2D data that follows a quadratic relationship. Train a decision tree regression and polynomial regression model. Compare the performance of both models on the test set and explain which is better suited for this data.
Q. 7) Generate a 2D linear dataset and train three models: decision tree regression, random forest, and gradient boosting. Evaluate and compare their performance using metrics such as R2R^2R2, RMSE, and MAE.
Metrics like ???? 2 R 2 , RMSE, and MAE compare performance. Random Forest and Gradient Boosting typically outperform Decision Trees on noisy data.
Q. 8) Create a regression dataset with noise added to the target variable. Train a Decision Tree Regressor and analyze its performance. Discuss overfitting and underfitting in this context. Apply pruning or control hyperparameters like max_depth to address overfitting. Compare results.
Analyze overfitting by observing performance on training vs. test sets. Use pruning (e.g., limit max_depth) to improve generalization.
Q. 9) Given a dataset with multiple features, train a Gradient Boosting Regressor. Analyze the feature importance and identify the top 3 features contributing to the predictions. Remove the least important features and observe the change in model performance.
Train a Gradient Boosting Regressor. Remove low-importance features. Analyze the change in metrics (e.g., RMSE, ???? 2 R 2 ).
Q. 10) Using a synthetic 2D dataset, train a Random Forest regressor. Experiment with different values of n_estimators, max_depth, and min_samples_split. Identify the combination of hyperparameters that minimizes test error. Visualize the learning curve for the best model.
Experiment with ???? estimators , max depth , min samples_split n estimators ,max depth ,min samples_split . Use grid search or random search to minimize test error. Plot the learning curve to visualize improvement.
Q. 11) You are given a dataset with a nonlinear relationship between the features and target. Train a decision tree regression model to fit the data and: Visualize the decision boundaries of the tree in a 2D space. Compare its performance with a linear regression model using RMSE and MAE.
Nonlinear Dataset: Visualize decision boundaries and compare RMSE/MAE. Linear regression fails on nonlinear data unless transformed.
Q. 12) Summarize the key findings of the paper. What are the main challenges highlighted in the development and deployment of data science models?
Managing bias and fairness. Ensuring interpretability and scalability. Handling incomplete or noisy data.
Q. 13) Discuss the role of human judgment in creating "data-driven" models, as explained in the paper. How do biases in feature selection, cleaning, or algorithm choice influence the outcomes?
Human biases in feature selection, cleaning, and algorithm choice influence outcomes. Balancing automation with human expertise is critical.
Q. 14) How can organizations balance automation with accountability in AI-driven decisions? Discuss the ethical implications of ignoring the human element in data science workflows.
Support their view with examples; propose topics like explainability in AI or fairness in automated systems.
Q. 15) Reflect on whether you agree or disagree with the authors’ conclusions and why. Propose further research topics to extend the paper’s findings.
Ethical implications arise when ignoring human oversight. Combine automated systems with human intervention to ensure fairness, accountability, and trustworthiness.
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