Machine Learning Question - Training AI to Detect Bots (Full mock interview)

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In this interview, Nathan discusses how to train a machine-learning model to flag potential bot accounts on social networks. He addresses the issue of class imbalance in the training data and suggests subsampling or oversampling based on the data set size. Nathan also discusses the trade-offs between different models and metrics for evaluating model performance.

Chapters (Powered by ChapterMe) -
00:00 - Intro
00:53 - Preventing bots and malicious actors on social networks
04:20 - Dataset handling subsampling, oversampling, intelligent training
06:31 - Collecting minority class examples for model learning
08:33 - Algorithmic model selection for class imbalance
12:05 - Model Ensembling Weights for models based on empirical distribution
13:02 - Methods for ensembling models
17:48 - Split training examples to avoid bias
21:36 - Classification metrics accuracy, precision, false positives
23:55 - F1 score, AUROC, and manual review
26:18 - Metrics for costeffective model evaluation
30:00 - Retraining model against bots, robust evaluation pipeline
31:34 - Data mining for bot classification
32:06 - Machine learning model training with gold standard labels
33:36 - Discussing pros and cons of adversarial training

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