Use float as percentage in min_samples_split and min_samples_leaf of DecisionTree

A float number can be used as percentage in these two parameters in case the sample size varies greatly.

I found a new parameter `min_weight_fraction_leaf` is added recently. It works in a similar way. But it is still convenient to use float in `min_samples_split` for those sample without weighting.
Here you could use the product function from itertools to reduce the number of nested level:

```
for max_leaf_nodes, name in product((None, 1000), ALL_TREES):
     TreeEstimator = ALL_TREES[name]
```