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- Underfitting occurs when a model is too simple to capture underlying patterns.
a) True
- Overfitting occurs when a model learns noise in the training data and performs poorly on new data.
a) True
- Python libraries like NumPy, Pandas, and scikit-learn are commonly used in ML workflows.
a) True
- Machine learning is a subset of artificial intelligence that focuses on learning patterns from data.
a) True
- Regression is a supervised learning task that predicts continuous numerical values.
a) True
- Model evaluation is unnecessary if a model performs well on training data.
a) False
- Machine learning guarantees perfect predictions in all situations.
a) False
- Ethical considerations can be relevant in machine learning applications.
a) True
- Labels represent the output variable in supervised learning.
a) True
- Python is widely used in machine learning because of its strong ecosystem of data science libraries.
a) True
- Data preprocessing may include handling missing values and scaling numeric features.
a) True
- Unsupervised learning requires labeled outcomes for training.
a) False
- The goal of machine learning is always to memorize the training data exactly.
a) False
- Features are the input variables used to make predictions.
a) True
- Machine learning models can be improved through iterative refinement and better data.
a) True
- The machine learning lifecycle typically includes data collection, preprocessing, model training, and evaluation.
a) True
- Real-world datasets often require cleaning before they can be used for machine learning.
a) True
- Supervised learning uses unlabeled data to train predictive models.
a) False
- A training dataset is primarily used to evaluate final model performance on unseen data.
a) False
- Classification is a supervised learning task that predicts categories.
a) True
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