Which term refers to the error introduced by the tendency of a model to fit the training data too closely, capturing noise?
- Bias
- Overfitting
- Underfitting
- Variance
Overfitting is the term used to describe when a model fits the training data too closely, capturing noise and leading to poor generalization on unseen data. It results in a high variance.
Loading...
Related Quiz
- When models are too simple and cannot capture the underlying trend of the data, it's termed as ________.
- A financial institution wants to predict whether a loan applicant is likely to default on their loan. They have a mix of numerical data (like income, age) and categorical data (like occupation, marital status). Which algorithm might be well-suited for this task due to its ability to handle both types of data?
- Which machine learning algorithm works by recursively splitting the data set into subsets based on the value of features until it reaches a certain stopping criterion?
- Ensuring that a machine learning model does not unintentionally favor or discriminate against certain groups is ensuring its ________.
- One of the challenges in training deep RNNs is the ________ gradient problem, which affects the network's ability to learn long-range dependencies.