figure the large-sample pointwise squared bias of the learner for the bias is zero for for our running example we can use ( to derive large-sample approximation of the pointwise variance of the learner gt (xx> bn in particularshow that for large var gt ( `xh- - xh- mphp ( figure shows this (large-samplevariance of the learner for different values of the predictor and model index observe that the variance ultimately increases in and that it is smaller at / than closer to the endpoints or since the bias is also figure the pointwise variance of the learner for various pairs of and larger near the endpointswe deduce that the pointwise mean squared error ( is larger near the endpoints of the interval [ than near its middle in other wordsthe error is much smaller in the center of the data cloud than near its periphery