Question

Q:9. Overfitting
1. The model remembers a huge
number of examples instead of learning
to notice features and may fail to predict
future observations reliably.
2. Occurs when a statistical model
cannot adequately capture the underlying
structure of the data.
3. Occurs if the model or algorithm
shows low variance but high bias
4.
None of the above​

Answer 1

Answer:

C)occurs if the model or algorithm shows low variance but high bias

Answer 2

The above additions are:

• The correct option is 3. Appears when model or algorithm Shows low variability but high bias.
• Overlaying is a mathematical error that occurs when the function is closely aligned with a limited set of data points.
• As a result, the model is useful for reference only to its original data set, and not for any other data sets.
• Subscribing occurs when a mathematical model or machine learning algorithm can capture a basic data trend.
• Clearly, under measurement occurs when a model or algorithm shows low variability but high bias.
• Often the result of an extremely simple model.