ML Fundamentals & Real-World Impact

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Test core ML concepts and their effects.

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DR. CLARA SHANTHI D

India

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ML Fundamentals & Real-World Impact


Froggy Jumps ML Fundamentals & Real-World ImpactVersión en línea Test core ML concepts and their effects. por DR. CLARA SHANTHI D 1 What best describes overfitting? a Model learns noise in training data, hurting generalization b Model fits data exactly with perfect accuracy c Model ignores training data and underfits 2 What is the bias-variance tradeoff? a Choosing the most complex model possible b Eliminating all training data noise c Balancing error due to simplistic assumptions and data noise 3 Why use a train/test split? a To assess generalization to unseen data b To perfectly memorize the test set c To maximize training data size only 4 Which learning type uses labeled data? a Unsupervised learning b Supervised learning c Reinforcement learning 5 What does gradient descent optimize? a Increase model complexity b Maximize training speed c Minimize the model loss function 6 Which metric is defined as correct predicted positives over all predicted positives? a Accuracy b Precision c Recall 7 What is model drift? a Model performance degrades over time due to changing data b Model weights become permanently fixed c Training data size resets automatically 8 Which technique helps explain model decisions locally? a Batch normalization b Standardization c LIME or SHAP 9 What does data quality impact most in ML? a Only data storage size b Training speed exclusively c Model accuracy and fairness 10 What is a key post-deployment practice? a Only retrain when requested by users b Monitor performance and fairness over time c Ignore updates unless errors occur

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ML Fundamentals & Real-World Impact

Froggy Jumps

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ML Fundamentals & Real-World ImpactVersión en línea

por DR. CLARA SHANTHI D

What best describes overfitting?

What is the bias-variance tradeoff?

Why use a train/test split?

Which learning type uses labeled data?

What does gradient descent optimize?

Which metric is defined as correct predicted positives over all predicted positives?

What is model drift?

Which technique helps explain model decisions locally?

What does data quality impact most in ML?

What is a key post-deployment practice?