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.

d. air pressure decreases as altitude increases.

  distance on the y-axis and time on the x-axis.

explanation:

the instantaneous velocity = change in position/time taken

the slope of the graph should give instantaneous velocity.

  slope of a graph = change in value of y -axis / change in values of x -axis

  comparing both the equations

    change in value of y -axis = change in position

  change in values of x -axis = time taken

so position values should be on the y axis and time values should be on the x axis.

distance on the y-axis and time on the x-axis.