1.4.0
Home
Installation
How to use this guide
Getting started
Support
User Guide
Tutorials
getML suite
Managing projects
Importing data
Annotating data
Data model
Preprocessing
Feature engineering
Predicting
Hyperparameter optimization
Deployment
API Documentation
Python API
data
database
datasets
engine
hyperopt
feature_learning
project
preprocessors
pipeline
predictors
ScaleGBMClassifier
ScaleGBMRegressor
LinearRegression
LogisticRegression
XGBoostClassifier
XGBoostRegressor
validate
booster
colsample_bylevel
colsample_bytree
early_stopping_rounds
external_memory
gamma
learning_rate
max_delta_step
max_depth
min_child_weights
n_estimators
n_jobs
normalize_type
num_parallel_tree
objective
one_drop
rate_drop
reg_alpha
reg_lambda
sample_type
silent
skip_drop
subsample
type
sqlite3
DataFrame
Pipeline
set_project
Command line interface
Integration
Fast API
About
getML
Docs
»
Python API
»
getml.predictors
»
XGBoostRegressor
»
num_parallel_tree
num_parallel_tree
¶
XGBoostRegressor.
num_parallel_tree
:
int
=
1
¶