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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Q. 12)    Summarize the key findings of the paper. What are the main challenges highlighted in the development and deployment of data science models?

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?

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.

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.

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