# Tag Info

1

Well, never = never, once a month = 1/30 per day, 1-2 per week = (say) 3/14 per day and so on. But 1) Fruits - 5 or more portions isn't a number. 2) Fruit juice frequency per week isn't in portions 3) A portion of juice isn't necessarily equal (in any real sense) to a portion of fruit. so, you can't really do this. I think you have to leave them as ...

3

It is common to use simple (unscaled) mean differences to understand and communicate experimental results. Furthermore, it is permissible to use them in meta-analyses. In meta-analyses, however, mean differences are rarely used. The reason is that studies often measure effects using different (incommensurate) scales. If one study were conducted using ...

0

Correlation is scale-invariant. Try > cor(zx, y) and you'll see that the correlation between the raw and z scored data is also the same.

2

Two facts: (i) Correlation is the covariance of the z-scores. (e.g. see here about four-fifths of the way down the page; alternatively, try zx = scale(x) # this returns z-scores directly, but you can use your form instead zy = scale(y) cov(zx,zy);cor(x,y) to see that covariance of z-scores and correlation are the same. (ii) If you takes z-scores of ...

1

Choose 1 when you don't have access to large unlabeled data Choose 2 when you do have access to large unlabeled data 3 is incorrect Ideally, your test and training data should have the same distribution, which implies that mean and variance should have matched. That is, 2] is simply a better estimate of mean than 1]. However, your improved result for 3] ...

2

Feature normalization is to make different features in the same scale. The scaling speeds up gradient descent by avoiding many extra iterations that are required when one or more features take on much larger values than the rest(Without scaling, the cost function that is visualized will show a great asymmetry). I think it makes sense that use the mean and ...

3

Your approach is entirely correct. Although data transformations are often undervalued as "preprocessing", one cannot emphasize enough that transformations in order to optimize model performance can and should be treated as part of the model building process. Reasoning: A model shall be applied on unseen data which is in general not available at the time ...

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