1. Basic machine learning concepts
  2. Probability Theory concepts
  • expectation
  • correlated random variable

Analysis of Committees1

The committee is a native inspiration for how to combine several models(or we can say how to combine the outputs of several models). For example, we can combine all the models by:

\[ y_{COM}(X)=\frac{1}{M}\sum_{m=1}^My_m(X)\tag{1} \]

Then we want to find out whether this average prediction of models is better than every one of them.

To compare the committee and a single model, we need first to build a criterion depending on which we can distinguish which result is better. Assuming that the true generator of the training data \(x\) is:

\[ h(x)\tag{2} \]

So our prediction of the \(m\)th model for \(m=1,2\cdots,M\) can be represented as:

\[ y_m(x) = h(x) +\epsilon_m(x)\tag{3} \] where \(\epsilon_m(x)\) is the error of \(m\) th model.Then the average sum-of-squares of error can be a nice criterion.

The criterion of a single model is:

\[ \mathbb{E}_x[(y_m(x)-h(x))^2] = \mathbb{E}_x[\epsilon_m(x)^2] \tag{4} \]

where the \(\mathbb{E}[\cdot]\) is the frequentist expectation(or average for usual saying).

Now we consider the average of the error over \(M\) models:

\[ E_{AV} = \frac{1}{M}\sum_{m=1}^M\mathbb{E}_x[\epsilon_m(x)^2]\tag{5} \]

And on the other hand, the committees have the error given by equations (1), (3), and (4):

\[ \begin{aligned} E_{COM}&=\mathbb{E}_x[(\frac{1}{M}\sum_{m=1}^My_m(x)-h(x))^2] \\ &=\mathbb{E}_x[\{\frac{1}{M}\sum_{m=1}^M\epsilon_m(x)\}^2] \end{aligned} \tag{6} \]

Now we assume that the random variables \(\epsilon_i(x)\) for \(i=1,2,\cdots,M\) have mean 0 and uncorrelated, so that:

\[ \begin{aligned} \mathbb{E}_x[\epsilon_m(x)]&=0 &\\ \mathbb{E}_x[\epsilon_m(x)\epsilon_l(x)]&=0,&m\neq l \end{aligned} \tag{7} \]

Then substitute equation (7) into equation (6), we can get:

\[ E_{COM}=\frac{1}{M^2}\mathbb{E}_x[\epsilon_m(x)]\tag{8} \]

According to the equation (5) and (8):

\[ E_{AV}=\frac{1}{M}E_{COM}\tag{9} \]

All the mathematics above is based on the assumption that the error of each model is uncorrelated. However, most time they are highly correlated and the reduction of error is generally small. But the relation:

\[ E_{COM}\leq E_{AV}\tag{10} \]

exists definitely which means committees can produce better predictions than a single model.


  1. Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.↩︎