class: center, middle, inverse, title-slide # Random Forests ### Dr. D’Agostino McGowan --- layout: true <div class="my-footer"> <span> Dr. Lucy D'Agostino McGowan <i>adapted from slides by Hastie & Tibshirani</i> </span> </div> --- ## Random forests _Do you_ ❤️ _all of the tree puns?_ * Random forests provide an improvement over bagged trees by way of a small tweak that _decorrelates_ the trees -- * By _decorrelating_ the trees, this reduces the variance even more when we average the trees! --- ## Random Forest process * Like bagging, build a number of decision trees on bootstrapped training samples -- * Each time the tree is split, instead of considering _all predictors_ (like bagging), **a random selection of** `\(m\)` **predictors** is chosen as split candidates from the full set of `\(p\)` predictors * The split is allowed to use only one of those `\(m\)` predictors -- * A fresh selection of `\(m\)` predictors is taken at each split -- * typically we choose `\(m \approx \sqrt{p}\)` --- class: inverse <div class="countdown" id="timer_5e8ce160" style="right:0;bottom:0;" data-warnwhen="0"> <code class="countdown-time"><span class="countdown-digits minutes">01</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> ## <i class="fas fa-edit"></i> `Choosing m for Random Forest` Let's say you have a dataset with 100 observations and 9 variables, if you were fitting a random forest, what would a good `\(m\)` be?