Alternating conditional expectations

<p>In <a href="/facts/Statistics/DNPsKGYU">statistics</a>, Alternating Conditional Expectations (ACE) is a <a href="/facts/Nonparametric_statistics/svTDJHTb">nonparametric</a> <a href="/facts/Algorithm/fnl5NmRt">algorithm</a> used in <a href="/facts/Regression_analysis/n6z5Tf7K">regression analysis</a> to find the optimal transformations for both the outcome (<a href="/facts/Response_variable/dINfzIF2">response</a>) variable and the input (predictor) variables. 
</p><p>For example, in a model that tries to predict house prices based on size and location, ACE helps by figuring out if, for instance, transforming the size (maybe taking the <a href="/facts/Square_root/AfrzfBdQ">square root</a> or logarithm) or the location (perhaps grouping locations into categories) would make the relationship easier to model and lead to better predictions. The algorithm iteratively adjusts these transformations until it finds the ones that maximize the <a href="/facts/Predictive_power/SBBA4w4s">predictive power</a> of the regression model.
</p>

Alternating conditional expectations open-in-new

Alternating conditional expectations