Which Of The Following Cannot Be Registered As A Trade Name?

Regularization

You are training a classification model with logistic regression. Which of the following statements are true? Cheque all that utilize.

Adding many new features to the model makes it more than likely to overfit the preparation set.
Calculation a new feature to the model ever results in equal or improve functioning on examples not in the training set.
Introducing regularization to the model always results in equal or better functioning on examples not in the training set.
Introducing regularization to the model ever results in equal or better performance on the training set.
Adding a new feature to the model always results in equal or better performance on the preparation ready.

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

Consider a classification problem. Calculation regularization may crusade your classifier to incorrectly classify some training examples (which information technology had correctly classified when not using regularization, i.east. when 0λ=0).
Using too large a value of λ tin cause your hypothesis to overfit the data; this can exist avoided by reducing λ.
Using a very big value of λ cannot hurt the operation of your hypothesis; the just reason nosotros exercise not set λ to be too large is to avoid numerical issues.
Because logistic regression outputs values 0≤hθ(x)≤1, its range of output values can but be "shrunk" slightly by regularization anyhow, and then regularization is more often than not not helpful for it.
Using too large a value of λ tin can crusade your hypothesis to underfit the data.

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