If a model has low bias and high variance, it is likely that the model is ________.
- Optimally Fitted
- Overfitting
- Underfitting
- Well-fitted
A model with low bias and high variance is likely overfitting. Low bias means the model fits the training data very well (potentially too well), and high variance indicates that it's very sensitive to fluctuations in the data, which can lead to poor generalization. Overfitting is a common outcome of this scenario.
Loading...
Related Quiz
- How do activation functions, like the ReLU (Rectified Linear Unit), contribute to the operation of a neural network?
- 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?
- A researcher is working with a large dataset of patient medical records with numerous features. They want to visualize the data in 2D to spot any potential patterns or groupings but without necessarily clustering the data. Which technique would they most likely employ?
- A machine learning model trained for predicting whether an email is spam or not has a very high accuracy of 99%. However, almost all emails (including non-spam) are classified as non-spam by the model. What could be a potential issue with relying solely on accuracy in this case?
- GRUs are often considered a middle ground between basic RNNs and ________ in terms of complexity and performance.