rtance like the same way
we did for random forest and trees.

---
class: middle
# Improvements:
# "extreme" gradient boosting

[XGBoost: A Scalable Tree Boosting System, 2016](http://dmlc.cs.washington.edu/data/pdf/XGBoostArxiv.pdf)
---
class: center, middle
# Speeding up tree-building via binning
---
# Split finding is slow

```python
for feature in features:
    for threshold in thresholds(f):
        gain = compute_gain(feature, threshold)
        if gain > best_gain:
            best_split = (feature, t