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- Features are the variables used as inputs to a supervised model.
a) True
- Accuracy is always the best metric for every classification problem.
a) False
- Cross-validation helps estimate model performance more reliably.
a) True
- Test data should be used during training to improve the model directly.
a) False
- Regression predicts continuous numeric values.
a) True
- A model can appear accurate on training data but fail on new data.
a) True
- Data preprocessing can improve supervised model performance.
a) True
- Feature engineering can influence supervised learning results.
a) True
- Imbalanced datasets can make accuracy misleading.
a)True
- The target variable is the value a supervised model tries to predict.
a) True
- Overfitting means the model generalizes well to unseen data.
a) False
- Labeled data quality affects model reliability.
a) True
- Training data is used to fit the model.
a) True
- Underfitting means the model is too complex for the data.True
a) False
- Supervised learning uses labeled datasets where outcomes are known.
a) True
- Precision measures how many predicted positives are actually positive.
a) True
- Logistic regression is commonly used for classification tasks.
a) True
- Confusion matrices summarize prediction outcomes for classification.
a) True
- Recall measures how many actual positives were correctly identified.
a) True
- Classification predicts continuous numeric values.
a) False
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