You are training a classification model with logistic regression.

Which of the following statements are true? Check all that apply.

•Adding a new feature to the model always results in equal or better performanceon examples not in the training set.

•Adding many new features to the model makes it more likely to overfit the trainingset.

•Introducing regularization to the model always results in equal or better perfor-mance on examples not in the training set.

•Introducing regularization to the model always results in equal or better perfor-mance on the training set.

The correct answers are A) Adding a new feature to the model always results in equal or better performance on examples, not in the training set and B) Adding many new features to the model makes it more likely to overfit the training set.

You are training a classification model with logistic regression.

The following statements are true: Adding a new feature to the model always results in equal or better performance on examples, not in the training set and Adding many new features to the model makes it more likely to overfit the training set.

When we are talking about statistical terms, logistic regression is the analysis used when the dependent variable is binary. This allows the researcher to explain and describe this variable and the interval. This logistic regression can be applied in medical research, engineering or social investigation.

B) Adding many new features to the model makes it more likely to over fit the training set.

Explanation:

Increasing number of features without increasing the amount of training data, increase over fitting in the model because features may be irrelevant and increase the redundant information in the model.