Combining Models

An average of several measurements is often more accurate than a single measurement. This happens when the errors of individual measurements more often cancel each other than reinforce each other. An average is also more stable than an individual measurement: if different sets of measurements are made on the same object, their averages would be more similar than individual measurements in a single set.

A similar phenomenon exists for predictive models: a weighted average of predictions is often more accurate and more stable than an individual model prediction. Though similar to what happens with measurements, it is less common and more surprising. A model relates inputs to a target. It seems surprising that a better relationship exists than is obtainable with a single model. Combining the models must produce a relationship not obtainable in any individual model.

An algorithm for training a model assumes some form of the relationship between the inputs and the target. Linear regression assumes a linear relation. Tree-based models assume a constant relation within ranges of the inputs. Neural networks assume a nonlinear relationship that depends on the architecture and activation functions chosen for the network.

Combining predictions from two different algorithms might produce a relationship of a different form than either algorithm assumes. If two models specify different relationships and fit the data well, their average is apt to fit the data better. If not, an individual model is apt to be adequate. In practice, the best way to know is to combine some models and compare the results.

For neural networks, applying the same algorithm several times to the same data might produce different results, especially when early stopping is used, since the results might be sensitive to the random initial weights. Averaging the predictions of several networks trained with early stopping often improves the accuracy of predictions.

An ensemble or committee is a collection of models regarded as one combined model. The ensemble predicts a target value as an average or a vote of the predictions of the individual model. The different individual models can give different weights to the average or vote.

For an interval target, an ensemble averages the predictions. For a categorical target, an ensemble might average the posterior probabilities of the target values. Alternatively, the ensemble might classify a case into the class that most of the individual models classify it. The latter method is called voting and is not equivalent to the method of averaging posteriors. Voting produces a predicted target value but does not produce posterior probabilities consistent with combining the individual posteriors.

Sometimes applying the same algorithm to slightly different data produces very different models. Stepwise regression and tree-based models behave this way when two important inputs have comparable predictive ability. When a tree creates a splitting rule, only one input is chosen. Changing the data slightly might tip the balance in favor of choosing the other input. A split on one input might segregate the data very differently than a split on the other input. In this situation, all descendent splits are apt to be different.

The unstable nature of tree-based models renders the interpretation of trees tricky. A business can continually collect new data, and a tree created in June might look very different from one created the previous January. An analyst who depended on the January tree for understanding the data is apt to become distrustful of the tree in June, unless he investigated the January tree for instability. The analyst should check the competing splitting rules in a node. If two splits are comparably predictive and the input variables suggest different explanations, then neither explanation tells the whole story.