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.
Unfortunately, this will create a regression

```
In [4]: RandomForestClassifier(min_samples_split=1.).fit(np.arange(6).reshape((3, 2)), np.arange(3))
Out[4]: 
RandomForestClassifier(bootstrap=True, compute_importances=None,
            criterion='gini', max_depth=None, max_features='auto',
            max_leaf_nodes=None, min_density=None, min_samples_leaf=1,
            min_samples_split=1.0, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0)
```