Mid-levelMultiple choiceDifferentiate between supervised, unsupervised, and reinforcement learning.AAll three learning types use the same data and methods.BSupervised learning uses unlabeled data, unsupervised learning uses labeled data, and reinforcement learning uses rewards and penalties.CSupervised learning uses labeled data, unsupervised learning uses unlabeled data, and reinforcement learning uses rewards and penalties.DSupervised learning uses rewards and penalties, unsupervised learning uses labeled data, and reinforcement learning uses unlabeled data.Check answer
Mid-levelMultiple choiceWhat is overfitting and how can it be prevented?AOverfitting occurs when a model is too simple; it can be prevented by adding more features.BOverfitting is when a model performs poorly on training data; it can be prevented by increasing the dataset size.COverfitting occurs when a model learns irrelevant details from training data; it can be prevented using cross-validation and regularization.DOverfitting is when a model performs well on new data; it can be prevented by using more complex models.Check answer
Mid-levelMultiple choiceExplain precision vs. recall.APrecision measures the accuracy of positive predictions, while recall measures the ability to identify all positive instances.BPrecision measures the ability to identify all positive instances, while recall measures the accuracy of positive predictions.CPrecision and recall are unrelated to model evaluation.DPrecision and recall both measure the accuracy of negative predictions.Check answer
Mid-levelMultiple choiceWhy are long-tail distributions important in classification and regression problems?AThey simplify the model training process.BThey can cause models to be biased toward the majority class and miss rare events.CThey have no impact on model performance.DThey ensure that the model will always have high accuracy.Check answer
JuniorMultiple choiceWhat is the primary difference between supervised and unsupervised learning?ASupervised learning does not require any data, while unsupervised learning requires a lot of data.BSupervised learning is faster than unsupervised learning.CSupervised learning uses labeled data, while unsupervised learning uses unlabeled data.DSupervised learning is used for clustering, while unsupervised learning is used for regression.Check answer
