Generally, the data points to be discriminated are projected onto w → {\displaystyle {\vec {w}}} ; then the threshold that best separates the data is chosen from analysis of the one-dimensional distribution. There is no general rule for the threshold. However, if projections of points from both classes exhibit approximately the same distributions, a good choice would be the hyperplane between projections of the two means, w → ⋅ μ → 0 {\displaystyle {\vec {w}}\cdot {\vec {\mu }}_{0}} and w → ⋅ μ → 1 {\displaystyle {\vec {w}}\cdot {\vec {\mu }}_{1}} .