Machine Learning Essentials

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1. 1

   What is supervised learning?

   A A learning method where models are trained on labeled data. B A learning method with no labeled data. C A learning method focused on reinforcement.
2. 2

   Which algorithm is commonly used for classification?

   A K-Means B Principal Component Analysis C Support Vector Machines
3. 3

   What does 'overfitting' refer to in machine learning?

   A A model that underperforms on the training data. B A model that has too few parameters. C A model that performs well on training data but poorly on new data.
4. 4

   What is the purpose of cross-validation?

   A To increase the size of the training dataset. B To assess how the model will generalize to an independent dataset. C To replace the need for a test dataset.
5. 5

   What is a 'hyperparameter'?

   A A parameter set before training a model. B A parameter learned during the training process. C A parameter that does not affect model training.
6. 6

   Which of these is an ensemble method?

   A Decision Trees B Random Forest C K-Nearest Neighbors
7. 7

   What is a neural network?

   A A ensemble learning method. B A computational model inspired by the way biological neural networks work. C A simple linear regression model.
8. 8

   Which metric is crucial for classification problems?

   A Mean Absolute Error B R-squared C F1 Score
9. 9

   What distinguishes reinforcement learning from other types?

   A It only uses labeled data. B It learns to make decisions by receiving rewards and punishments. C It requires no data to function.
10. 10

    Which of the following is an unsupervised learning technique?

    A K-Means Clustering B Logistic Regression C Recurrent Neural Networks