Basis Lab

Trees, and Committees of Them

When to Reach for a Tree

Last timeWhich Input Actually Mattered

On data that lives in a spreadsheet a committee of trees usually wins, on data made of pixels, sound or words it usually loses, and the shape of the data says which case you are in.

The claim, stated plainly

On data that arrives as rows and columns with names on the columns, a

committee of trees is usually the most accurate thing available and takes a

few minutes to fit. On data that arrives as pixels, audio samples or text, a

network wins and nothing in this course comes close.

That division is sharp enough to be useful as a rule, and the reason for it is

specific rather than mysterious.

Why the division falls where it does

A split asks about one column against a threshold, which presumes that the

column means something on its own. Income above thirty thousand is a

meaningful statement. That is the usual situation in a spreadsheet, where

somebody chose and named each column because it meant something.

Now consider a photograph. The value at pixel four thousand and twelve means

nothing on its own; it means something only in relation to its neighbours, and

the same object appearing twenty pixels to the left is an entirely different

set of column values. A tree would have to rediscover that relation one

question at a time, and it cannot.

FIG 1Which family, by what the inputs are
a single input means somwhich family winstypical gap
spreadsheet columns, mix113
sensor readings over tim111
pixels of an image0240
audio samples0250
words of a document0235
The middle column is 1 for trees and 2 for networks, and the last is a rough gap in accuracy points. The first column predicts the second exactly. Where a single input means something, trees win by a little; where it does not, networks win by a great deal, which is why the right question is about the inputs rather than about the method.

The gaps are asymmetric and that matters for the decision. Choosing a network

for spreadsheet data costs a few points and a great deal of time. Choosing a

tree for images costs everything.

The lesson stops here

6 more paragraphs to go

You have read the opening. The rest of the argument, the problems that check whether it landed, and the lines worth keeping at the end all come with a plan.

The first lesson of every course in the library reads the whole way through, free, so you can see exactly what the rest of them are.

See the planThe contents

This is the reading half

Starting the course gives you your own copy of it. Every idea on every page has problems standing under it, marked with a reason rather than a tick, and any sentence you do not believe can be opened and argued with. None of that can happen on a page nobody owns.

The contents

The rest of this course

  1. 01Splitting on One Question
  2. 02Growing a Whole Treeopening only
  3. 03Knowing When to Stop Growingopening only
  4. 04One Tree Is Unstableopening only
  5. 05Averaging Many Treesopening only
  6. 06Each Tree Fixing the Lastopening only
  7. 07Which Input Actually Matteredopening only
  8. 08When to Reach for a Treeyou are here

Read alongside