Machine Learning Fundamentals Questions
Core machine learning concepts and terminology for conceptual understanding. Topics include supervised and unsupervised learning, regression and classification problems, training validation and test splits, cross validation, loss functions and optimization at a high level, model evaluation metrics, overfitting and underfitting, regularization concepts, and common basic model families such as linear models decision trees nearest neighbors and simple neural networks. Emphasis is on conceptual explanations and trade offs rather than deep mathematical derivations.
MediumTechnical
96 practiced
Explain model calibration and why predicted probabilities can be miscalibrated. Describe two common post-processing techniques to improve calibration and one method to evaluate calibration quality.
MediumTechnical
89 practiced
Compare decision trees and linear models on the following axes: interpretability, feature interactions, robustness to outliers, latency at prediction time, and ease of calibration. For each axis, state which model family is generally stronger and why.
EasyTechnical
83 practiced
You have a binary classifier for fraud detection with 99.9% accuracy on a dataset where fraud is 0.1% of events. Explain why accuracy is misleading here and name three evaluation metrics that better capture model utility for this problem. Briefly state what each measures.
EasyTechnical
102 practiced
List the basic model families: linear models, decision trees, k-nearest neighbors (k-NN), and simple feedforward neural networks. For each, give one advantage and one limitation in production settings.
EasyTechnical
83 practiced
List common loss functions used for regression and classification (name and one-sentence description of when to use each). Include at least three regression losses and three classification losses.
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