is it necessary to center and scale data before predicting?
<p>In the train function of the caret package it is possible to perform centering and scaling of predictors as in the following example:</p>

<pre><code>knnFit &lt;- train(Direction ~ ., data = training, method = "knn",
                preProcess = c("center","scale"))
</code></pre>

<p>Setting this transformation in train should give a better evaluation of the performance of the algorithm during resampling.</p>

<p>In this case when I use the model to predict the response for new data should I care about centering and scaling or this operation is included in the final model?</p>

<p>Is the following operation sufficient?</p>

<pre><code>pred &lt;- predict(knnFit, newdata = test)
</code></pre>

<p>Thanks!</p>