A statistical model is developed by training the machine learning algorithm using training data. In most...
- A statistical model is developed by training the machine
learning algorithm using training data. In most
cases, this is just a subset of all the possible data for the
problem for which the model is being developed. We want to develop
a model that also works well with unseen data, called test
data. The models that we build can
overfit or underfit the data.
With this in mind, which of the following statements is false:
- Using Ridge regression versus linear regression on a dataset can improve predictability by reducing overfitting.
- A model that underfits the data, does not predict well on the test data, and does not predict well on the training data because there are too many features in the model.
- Overfitting occurs when the model fits too well on the training data, and the model does not fit as well on the test data.
- Eliminating some of a model’s less significant features can help reduce overfitting.
Homework Answers
B).A model that underfits the data, does not predict well on the test data, and does not predict well on the training data because there are too many features in the model:
We develop statistical model with the help of training data we train our algorithm which we wrote for machine learning. The below options are made to model the processes of the test data the statement which is not true in this case is option B which is totally wrong. Over lifting always plays a part it means that it is not the incorrect option then we are left with option A and B. Ridge regression versus linear regression we all know what they do in the case what doesn't is option B
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