in contribution data-science python ~ read.

How to use XGBoost in Python

It has been some time since I discovered Kaggle-winning estimator XGBoost.

I have successfully used that in several projects and it always performed quite well. If it wasn't the best estimator, usually it was one of the best.

Personally, I like it because it solves several problems:

  1. accepts sparse datasets - that means no additional steps in cases when it is hard or impossible to solve data imputation (specially with extremely sparse datasets, such as those coming from poll or surveys)
  2. fast - written in C++ with focus on efficiency
  3. Python API and easy installation using pip - all I had to do was pip install xgboost (or build it and do the same). I use Python for my data science and machine learning work, so this is important for me.
  4. scikit-learn interface - fit/predict idea, can be used in all fancy scikit-learn routines, such as RandomizedSearchCV, cross-validations and so on.
  5. very versatile - supports regression as classification problems, has plenty of parameters to adjust its bias/variance or time needed to fit and learn

Disadvantage is its not-so-well documentation. It has publication of some API and some examples, but they are not very good. E.g. it is not clear what parameter names should be used in Python (to what parameters it corresponds in the core package).

I wasn't able to use XGBoost (at least regressor) on more than about hundreds of thousands of samples. About milion or so it started to be to long to be used for my usage (e.g. days of training time or simple parameter search).

How to use it in Python

Let's prepare some data first:

from sklearn import datasets  
from sklearn.model_selection import train_test_split  
from sklearn.model_selection import cross_val_score

X, y = datasets.make_classification(n_samples=10000, n_features=20,  
                                    n_informative=2, n_redundant=10,
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,  

I will present the scikit-learn interface. If you are familiar with that one, these lines should be obvious to you:

from xgboost.sklearn import XGBClassifier  
from xgboost.sklearn import XGBRegressor

xclas = XGBClassifier()  # and for classifier, y_train)  

and as I said, since it expose scikit-learn API, you can use as any other classifier:

cross_val_score(xclas, X_train, y_train)  

How to use XGBoost with RandomizedSearchCV

Are you still using classic grid search? Just don't and use RandomizedSearchCV instead.

Below is an example how to use scikit-learn's RandomizedSearchCV with XGBoost with some starting distributions. Of course, you should tweak them to your problem, since some of these are not invariant against the regression loss!

So, a sane starting point may be this. I use it for a regression problems.

First, prepare the model and paramters:

from xgboost.sklearn import XGBRegressor  
import scipy.stats as st

one_to_left = st.beta(10, 1)  
from_zero_positive = st.expon(0, 50)

params = {  
    "n_estimators": st.randint(3, 40),
    "max_depth": st.randint(3, 40),
    "learning_rate": st.uniform(0.05, 0.4),
    "colsample_bytree": one_to_left,
    "subsample": one_to_left,
    "gamma": st.uniform(0, 10),
    'reg_alpha': from_zero_positive,
    "min_child_weight": from_zero_positive,

xgbreg = XGBRegressor(nthreads=-1)  

and then just plug it into RS:

from sklearn.model_selection import RandomizedSearchCV

gs = RandomizedSearchCV(xgbreg, params, n_jobs=1), y_train)  

As you can see, I use nthreads=-1 and n_jobs=1. nthreads should be IMHO as well called n_jobs to follow sklearn conventions, but currently it is not. I explicitly mention this here because I usually set Search's n_jobs=-1, but here it would mean that XGBoost would run thread for every CPU + RS would create job for every CPU, effectively wasting cores by switching between N*N processes instead of just N... And in XGBoost I prefer setting nthreads, since I believe they have better paralelization than it is in Python (because of GIL ans stuff), but someone should test this...

And that's it! Enjoy!