the preceding graph shows all the points (as dotsand our fit (the solid linethis does not look very good in factusing this one-dimensional modelwe understand that house price is multiple of the rm variable (the number of roomsthis would mean thaton averagea house with two rooms would be double the price of single room and with three rooms would be triple the price we know that these are false assumptions (and are not even approximately trueone common step is to add bias term to the previous expression so that the price is multiple of rm plus bias this bias is the base price for zero-bedroom apartment the trick to implement this is to add to every element of xx boston data[:, np array([[ , for in ]we now use [ , instead of [vy boston target (slope,bias), , , np linalg lstsq( ,