Why decision trees is the best data mining algorithm

There is no such thing as a "best" data mining algorithm. Almost all the advantages you mentioned for decision trees, a form of recursive binary partitioning, applies to a greater extent to Random Forests, which are bootstrapped decision trees that only consider a subset of features at each node.

Examples of domains where decision trees perform poorly include: -Low amount of data -Domains where you have extra knowledge about the data (such as some features coming from certain probability distributions) that you can incorporate into classifiers.

Decision trees work well in a variety of applications, but that does not make them the "best" algorithm, and it is rare that a classical decision tree provides state of the art performance on any given data set. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.122...

Decision trees are useful for the points enumerated in the blog article.

One disadvantage of decision trees is that they can be slow on large data sets (> 1M examples). They are a batch algorithm, which means that you have look at all examples to build a tree, although they can be trained in an mini-online setting (only look at 10K examples per tree) which is faster.

More importantly, decision tree induction involves combinatorial optimization over splitting features. This is much slower than a continuous optimization over non-linearities. So decision tree induction is slower than, say, stochastic gradient descent over a neural network.

(As hamner points out, there is no "best" data mining algorithm, just like there is no "best" programming language.)

This article is weird. It makes an odd point, it includes the words "decision trees" seventeen times, and doesn't have much content. It feels like SEO bait.

Decision trees suffer from high variance. A slightly different sample might give you entirely different splits; using decision trees for data interpretation or feature selection is an art at best (and for some data sets uselessly unreliable).

>Decision trees are weak learners.

This is untrue. They're only used as weak learners in boosting because the tree depth is limited to some small constant.

>Decision trees run fast even with lots of observations and variables

I don't know all the decision tree learning algorithms, but at least some of the common ones run in O(features * samples * splits). That's not terrible, but you can handle much larger data sets optimizing w/ stochastic gradient descent or coordinate descent.

>Decision trees can easily handle unbalanced datasets.

This links to a post about bagging, which is not really specific to decision trees (but can be done with any learning algorithm)

No it is not. It depends on the structural form of what you are learning. Check http://www.pnas.org/content/105/31/10687.short for some other forms.

Decision trees are a learning system, and like all other tools, it has pros and cons. They are designed for state spaces where data can be easily divided up at each branching point, thus, they do not handle stochastic domains very well compared to something like a Bayesian network.

There is no silver bullet in data mining/machine learning.

They're nice when they work and when they're appropriate.

Spoilers: that's not always.

In this article there's a small screen shot of an application that looks like a decision tree designer ... does anyone know what this is? And is it public domain software?