Supervised Learning Flashcards
What is supervised learning?
Supervised learning is a type of machine learning where the model is trained on a labeled dataset.
True or False: Supervised learning requires labeled data.
True
What are the two main types of supervised learning?
Classification and regression
In supervised learning, what is the input data called?
Features
What is the output of a supervised learning model?
Predictions or labels
What is the goal of regression in supervised learning?
To predict continuous values
What is the goal of classification in supervised learning?
To predict discrete labels
What is the difference between classification and regression in supervised learning?
Classification predicts discrete labels, while regression predicts continuous values.
What is a hyperparameter in supervised learning?
A parameter whose value is set before the learning process begins.
What is overfitting in supervised learning?
When a model learns the noise in the training data rather than the underlying pattern.
What is underfitting in supervised learning?
When a model is too simple to capture the underlying structure of the data.
What is the loss function used in supervised learning?
A function that measures how well a model’s predictions match the actual labels.
What is cross-validation in supervised learning?
A technique used to evaluate the performance of a model by splitting the data into multiple subsets.
What is the bias-variance tradeoff in supervised learning?
The tradeoff between a model’s ability to capture complex patterns and its sensitivity to noise.
What is the purpose of a validation set in supervised learning?
To tune hyperparameters and evaluate the model’s performance.
