| title | layout |
|---|---|
Notes on xgboost: Extreme Gradient Boosting Machine |
default |
- Computation in C++
- tree-based models
- easy to use, install, R/python interface
- Automatic parallel computation on a single Machine
| import hashlib as hasher | |
| import datetime as date | |
| # Define what a Snakecoin block is | |
| class Block: | |
| def __init__(self, index, timestamp, data, previous_hash): | |
| self.index = index | |
| self.timestamp = timestamp | |
| self.data = data | |
| self.previous_hash = previous_hash |
| import hashlib as hasher | |
| class Block: | |
| def __init__(self, index, timestamp, data, previous_hash): | |
| self.index = index | |
| self.timestamp = timestamp | |
| self.data = data | |
| self.previous_hash = previous_hash | |
| self.hash = self.hash_block() | |
| title | subtitle | layout |
|---|---|---|
Notes from Statistical Learning Course |
via Stanford University Online |
default |
| import sys | |
| salesTotal = 0 | |
| oldKey = None | |
| for line in sys.stdin: | |
| data = line.strip().split("\t") | |
| if len(data) != 2: | |
| # Something has gone wrong. Skip this line. | |
| continue |