<p>For instance, would the following question be on-topic?</p>
<blockquote>
<p>In the context of training a decision tree, let <span class="math-container">$Y$</span> be the dependent
variable and <span class="math-container">$X_1, \dots X_d$</span> the independent variables.</p>
<p>In a typical implementation of a decision tree, the conditions used to
split the dependent variable only involve a single dependent variable
(e.g. <span class="math-container">$X_2 &gt; t_{X_2}$</span>). However, some implementations use conditions
(called <em>oblique</em>) that involve more than a single feature (e.g. <span class="math-container">$X_1+ X_2 &gt; t_{f(X_1, X_2)}$</span>).</p>
<p>In general, an oblique condition can be written as:</p>
<p><span class="math-container">$$  \sum_{j=0}^{k} w_iX_j \space &lt; \space  \mu_{X_1, \dots, X_n} $$</span></p>
<p>Since the number of features appearing in the condition <span class="math-container">$k$</span> and the
weights of those features <span class="math-container">$w_i$</span> and the features themselves and the
right member of the inequality can all change, this means that there
is a very large number of possible conditions to split on, each
having an associated measure of impurity that has to be calculated. How do the
implementations of oblique decision trees go about addressing this
issue?</p>
</blockquote>
