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docs: replace houseprice dataset with sklearn's fetch_california_housing (#692) #918

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docs: replace houseprice dataset with sklearn's fetch_california_housing (#692) #918

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docs: replace houseprice dataset with sklearn's fetch_california_hous…
snehagahlot3 Mar 22, 2026
f1ed2f4
fix: correct indentation in GeometricWidthDiscretiser.rst
snehagahlot3 Mar 22, 2026
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17 changes: 8 additions & 9 deletions docs/user_guide/discretisation/GeometricWidthDiscretiser.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -55,22 +55,21 @@ Let's load the house prices dataset and separate it into train and test sets:

from feature_engine.discretisation import GeometricWidthDiscretiser

# Load dataset
data = pd.read_csv('houseprice.csv')

# Separate into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
data.drop(['Id', 'SalePrice'], axis=1),
data['SalePrice'], test_size=0.3, random_state=0)

from sklearn.datasets import fetch_california_housing
data = fetch_california_housing(as_frame=True).frame
# Separate into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
data.drop(['MedHouseVal'], axis=1),
data['MedHouseVal'], test_size=0.3, random_state=0)


Now, we want to discretise the 2 variables indicated below into 10 intervals of increasing
width:

.. code:: python

# set up the discretisation transformer
disc = GeometricWidthDiscretiser(bins=10, variables=['LotArea', 'GrLivArea'])
disc = GeometricWidthDiscretiser(bins=10, variables=['MedInc', 'AveRooms'])

# fit the transformer
disc.fit(X_train)
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30 changes: 18 additions & 12 deletions docs/user_guide/wrappers/Wrapper.rst
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Expand Up @@ -33,16 +33,20 @@ impute only the selected variables.
from feature_engine.wrappers import SklearnTransformerWrapper

# Load dataset
data = pd.read_csv('houseprice.csv')
from sklearn.datasets import fetch_california_housing
data = fetch_california_housing(as_frame=True).frame


# Separate into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
data.drop(['Id', 'SalePrice'], axis=1),
data['SalePrice'], test_size=0.3, random_state=0)
data.drop(['MedHouseVal'], axis=1),
data['MedHouseVal'], test_size=0.3, random_state=0)


# set up the wrapper with the SimpleImputer
imputer = SklearnTransformerWrapper(transformer = SimpleImputer(strategy='mean'),
variables = ['LotFrontage', 'MasVnrArea'])
variables = ['MedInc', 'AveRooms'])


# fit the wrapper + SimpleImputer
imputer.fit(X_train)
Expand All @@ -64,16 +68,17 @@ to standardize only the selected variables.
from feature_engine.wrappers import SklearnTransformerWrapper

# Load dataset
data = pd.read_csv('houseprice.csv')
from sklearn.datasets import fetch_california_housing
data = fetch_california_housing(as_frame=True).frame

# Separate into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
data.drop(['Id', 'SalePrice'], axis=1),
data['SalePrice'], test_size=0.3, random_state=0)
data.drop(['MedHouseVal'], axis=1),
data['MedHouseVal'], test_size=0.3, random_state=0)

# set up the wrapper with the StandardScaler
scaler = SklearnTransformerWrapper(transformer = StandardScaler(),
variables = ['LotFrontage', 'MasVnrArea'])
variables = ['MedInc', 'AveRooms'])

# fit the wrapper + StandardScaler
scaler.fit(X_train)
Expand All @@ -95,12 +100,13 @@ to select only a subset of the variables.
from feature_engine.wrappers import SklearnTransformerWrapper

# Load dataset
data = pd.read_csv('houseprice.csv')

from sklearn.datasets import fetch_california_housing
data = fetch_california_housing(as_frame=True).frame

# Separate into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
data.drop(['Id', 'SalePrice'], axis=1),
data['SalePrice'], test_size=0.3, random_state=0)
data.drop(['MedHouseVal'], axis=1),
data['MedHouseVal'], test_size=0.3, random_state=0)

cols = [var for var in X_train.columns if X_train[var].dtypes !='O']

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