MaxHalford

import lightgbm as lgb
from sklearn import datasets
from sklearn import model_selection

X, y = datasets.load_boston(return_X_y=True)
X_fit, X_val, y_fit, y_val

model = lgb.LGBMRegressor()
model.fit(X_fit, y_fit)

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MaxHalford

>>> from creme import stats

>>> mean = stats.Mean()

>>> mean.update(5)
>>> mean.update(10)
>>> mean
7.5

>>> mean.update(6)

MaxHalford

>>> from creme import feature_extraction
>>> from creme import stats

>>> X = [
...     {'place': 'Taco Bell', 'revenue': 42},
...     {'place': 'Burger King', 'revenue': 16},
...     {'place': 'Burger King', 'revenue': 24},
...     {'place': 'Taco Bell', 'revenue': 58},
...     {'place': 'Burger King', 'revenue': 20},
...     {'place': 'Taco Bell', 'revenue': 50}

MaxHalford

>>> from creme import compose
>>> from creme import datasets
>>> from creme import linear_model
>>> from creme import metrics
>>> from creme import preprocessing

>>> X_y = datasets.Phishing()

>>> model = compose.Pipeline(
...     preprocessing.StandardScaler(),

MaxHalford

MaxHalford

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MaxHalford