Why is dropping predictors based on low correlation between predictors and the dependent variable before performing cross validation incorrect?
<p>Say I have predictors <code>X1</code>, <code>X2</code>, ..., <code>Xn</code> and a dependent variable <code>Y</code>.</p>

<p>I check correlation between the predictors and <code>Y</code> and drop predictors that have a low correlation with <code>Y</code>. Now I use cross validation between <code>Y</code> and the remaining predictors to train a logistic regression model.</p>

<p>What is wrong with this method?</p>