Essentially what that rule breaks into is:

`IF (nuclei_Intensity_MADIntensity_mito > 0.0037465095520019531,`

For a given cell/nucleus, determine if the MAD of the intensity in the mito channel is > or =< ~0.00375

`[-0.66219538337257688, 0.66219538337257688], `

If it IS, subtract 0.66 from its “total likelihood score that it belongs in the first bin”, and add 0.66 to its “total likelihood score that it belongs in the second bin”

`[0.75535238846532238, -0.75535238846532238])`

If it ISN’T, add 0.75 to its “total likelihood score that it belongs in the first bin”, and subtract 0.75 to its “total likelihood score that it belongs in the second bin”

At the end, it will add up the total likelihood scores for each bin, and put it in the bin with the highest score.

With two classes, the numbers for each class are symmetric like that, because there are only two possible bin choices, but imagine you had 3 classes- dim cells, bright triangular cells, and bright round cells:

A shape measurement might not tell you much about whether it belongs in bin1 but will tell you a lot about whether it belongs in bins 2 or 3, so the pattern for a measure that signifies high roundness might give you rule weights of [number_near_0, big_negative_number, big_positive_number], [number_near_0, big positive_number, big_negative_number])

An intensity measurement tells you if something belongs in bin 1 but not how it breaks into bins 2 vs 3, so a measurement of brightness <0.01 might give you rule weights of [big_positive_number, big_negative_number, big_negative_number], [big_negative_number, middleish_positive_number, middleish_positive_number])