I started with Gini vs entropy and felt pretty solid there, but when they pushed on post-pruning specifically I fumbled a bit.
Structure your answer as a logical pipeline: start with the recursive partitioning process, then explain how splits are chosen using impurity metrics, and finally cover stopping criteria and pruning. Emphasize the bias-variance trade-off and how hyperparameters like max_depth and min_samples_leaf control overfitting.
Pro tip: Mention that decision trees are greedy and prone to overfitting, so you must discuss both pre-pruning (early stopping) and post-pruning (cost-complexity pruning) with concrete hyperparameters. Also, tie it back to Amazon's scale by noting that for large datasets, approximate split finding (e.g., histogram-based) is often used.
Explain that training starts with all data at the root and recursively splits the feature space to maximize homogeneity of the target within child nodes.
Describe how candidate splits are evaluated using impurity measures (Gini, entropy for classification; MSE for regression) and the best split is chosen greedily.
List pre-pruning hyperparameters like max_depth, min_samples_split, min_samples_leaf, and max_features that halt tree growth early.
Explain post-pruning techniques such as cost-complexity pruning (e.g., ccp_alpha in scikit-learn) that remove branches that add little predictive power.
Summarize how the combination of pre-pruning, pruning, and ensembling (e.g., random forests, gradient boosting) mitigates overfitting and improves generalization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with K-Means, DBSCAN, and hierarchical.
Select three diverse clustering algorithms (e.g., K-means, DBSCAN, Hierarchical) and for each, clearly state the core idea in one sentence, then briefly explain how it works and when it is appropriate. Keep the explanation concise and focused on the intuition, avoiding unnecessary mathematical details unless asked.
Pro tip: Tie each algorithm to a practical use case or Amazon-scale scenario (e.g., customer segmentation, anomaly detection) to show business impact and depth beyond textbook knowledge.
Pick three algorithms that cover different clustering paradigms (centroid-based, density-based, hierarchical) to demonstrate breadth. For example: K-means, DBSCAN, and Hierarchical Clustering.
For each algorithm, give a one-sentence summary of its core idea. For K-means: partition data into K clusters by minimizing distance to centroids. For DBSCAN: group points that are closely packed together, marking outliers as noise. For Hierarchical: build a tree of clusters by merging or splitting based on distance.
Add 1-2 sentences on the mechanism: K-means iteratively assigns points and updates centroids; DBSCAN expands clusters from core points based on density; Hierarchical uses linkage criteria to merge/split clusters.
Highlight when each algorithm shines and its limitations: K-means is fast but assumes spherical clusters and needs K; DBSCAN finds arbitrary shapes and handles noise but struggles with varying densities; Hierarchical provides a dendrogram but is computationally expensive.
Give a brief example of how each could be used in a data science context, especially at Amazon scale (e.g., customer segmentation, fraud detection, product categorization).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.