Research task (AI & ML):
Write a blog post describing the pros and cons of using a gradient boosting model over a support vector machine for a health dataset. Some thing to consider when writing this post is how different feature selection methods can influence performance for both type of models. 

Evaluation rubric:
criterion: The response explicitly defines the goal of using machine learning in the healthcare industry and different types of tasks that can benefit from machine learning (e.g. disease diagnosis, medical imaging, personalized medicine, patient risk prediction).
weight: 3
axis: Implicit Criteria

criterion: The response explains why gradient boosting and Support Vector Machine are popular or significant compared to other models when working with health data (e.g. high accuracy, robustness to dataset diversity, high dimensionality, strong generalization).
weight: 3
axis: Synthesis of Information

criterion: The response describes at least three challenges with using health dataset from the following: class imbalance, high dimensionality, missing data, heterogeneity, and privacy.
weight: 3
axis: Implicit Criteria

criterion: The response provides description of both gradient boosting and Support Vector Machine as well as an intuitive explanation of their implementation (e.g. gradient boosting iteratively corrects errors to improve the model, SVM is a geometric approach using hyperplace to separate classes).
weight: 3
axis: Implicit Criteria

criterion: The response discusses different techniques of gradient boosting or Support Vector Machine that can be used to mitigate the common challenges of using health dataset (e.g. XGBoost can scale weights or use custom loss functions to address class imbalance, XGBoost can specify weights for each classes, SVM can adjust class weights or different error penalties for each class to address class imbalance, SVM can use appropriate kernel to support high dimensionality).
weight: 3
axis: Synthesis of Information

criterion: The response mentions gradient boosting does not require feature scaling while Support Vector is not scale-invariant because they rely on distance measures to the optimal hyperplane.
weight: 3
axis: Implicit Criteria

criterion: The response provides three major feature selection methodologies (filter, wrapper, and embedded) and discusses how each methodology works (e.g. filter method eliminates less significant features, wrapper method uses performance of a wrapper model as objective function, wrapper method uses forward selection to iteratively add features that most improves model performance, embedded method has feature selection as integral part of model training).
weight: 5
axis: Explicit Criteria

criterion: The response discusses impact of each three major feature selection (filter, wrapper, and embedded) on both gradient boosting and Support Vector Machine (e.g. filter method lowers high computational cost of SVM, wrapper method is a natural fit for SVM with Recursive Feature Elimination as a key example, Gradient Boosting Machine has strong embedded feature selection mechanism, filtering method is less critical for gradient boosting).
weight: 5
axis: Explicit Criteria

criterion: The response mentions importance of hyperparameter tuning and the relevant hyperparameters for both gradient boosting and Support Vector Machine (e.g. gradient boosting requires intensive hyperparameter tuning to balance performance and prevent overfitting, increasing max_depth of gradient boosting increases complexity of weak learners and captures more specific interactions, SVM can control the regularization parameter C to balance margin maximization and training error, SVM can change kernel to have non-linear shape of the decision boundary).
weight: 2
axis: Implicit Criteria

criterion: The response provides an example of a concrete list of feature selection strategy for gradient boosting and Support Vector Machine on health dataset (e.g. use feature_importances attribute to rank features for XGBoost, use SelectFromModel to automate thresholding process, fill in missing values using imputation, use PCA to reduce dimensionality).
weight: 1
axis: Implicit Criteria

criterion: The response provides at least two different evaluation metrics for using gradient boosting and Support Vector Machine on health related tasks (e.g. AUC for XGBoost to mitigate class imbalance, F1-score for SVM to mitigate class imbalance, precision-recall curve for SVM to mitigate class imbalance, confusion matrix for GBM). 
weight: 1
axis: Implicit Criteria

criterion: The response provides an example of how gradient boosting and Support Vector Machine performs on a health related task and compares them (e.g. XGBoost v.s. SVM on predicting risk of cardiovascular events, Light GBM v.s. SVM on classifying tumors in brain MRI, XGBoost v.s. SVM-RFE on protein classification from mass spectrometry data, CatBoost v.s. SVM-RFE on gene identification).
weight: 2
axis: Synthesis of Information

criterion: The response mentions at least three popular health dataset (e.g. Medical Information Mart for Intensive Care, The Cancer Genome Atlas,  CheXpert, Alzheimer's Disease Neuroimaging Initiative) and their characteristics (e.g. structured, unstructured image, volume, dimension).
weight: 1
axis: Implicit Criteria

criterion: The response discusses at least three key aspects of model interpretability for gradient boosting and Support Vector Machine (e.g. linear SVM is highly interpretable, non-linear kernels are essentially "black box" for SVM, Gradient Boosting Machine is not interpretable with hundreds of trees, SHapley Additive exPlanations can be used for GBM interpretability).
weight: 4
axis: Explicit Criteria

criterion: The response articulates at least one specific, practical use case of each gradient boosting and Support Vector Machine using health dataset (e.g. predict patient's risk of hospital readmission using XGBoost, classify tumors in brain MRI scans using SVM, identify subset of genes that can classify cancer using SVM-RFE, predict Intensive Care Unit mortality using CatBoost).
weight: 2
axis: Implicit Criteria

criterion: The response mentions computational cost of gradient boosting and Support Vector Machine and how they scale with training dataset size(e.g. SVM has high computational cost, non-linear kernels can be O(n^2) to O(n^3) given training dataset size n, XGBoost and LightGBM are relatively fast and memory efficient, gradient boosting supports distributed training).
weight: 4
axis: Explicit Criteria

criterion: The response discusses inherent robustness of gradient boosting and Support Vector Machine against noisy data (e.g. SVM with a small C ignores outliers, gradient boosting's each iteration trains a new tree to correct large errors, XGBoost and LightBGM's L1 and L2 regularization prevents trees from fitting to random noise, tree pruning of gradient boosting helps to cut off a few noisy data points).
weight: 3
axis: Implicit Criteria

criterion: The response is written in a blog style with conversational tone, clear structure, at least 1 visual aid including a figure, table or chat, and/or call to action.
weight: 5
axis: Instruction Following

criterion: The response provides a clear summary of the comparison between gradient boosting and Support Vector Machine and key takeaways.
weight: 2
axis: Communication Quality

criterion: The response states technical jargons (e.g. hyperplane, Recursive Feature Elimination, kernel trick, feature scaling) without providing a clear, simple explanation. 
weight: -2
axis: Communication Quality

criterion: The response is longer than 3000 words and is not suitable for a blog.
weight: -3
axis: Instruction Following