📝 Exercise M1.05

📝 Exercise M1.05#

The goal of this exercise is to evaluate the impact of feature preprocessing on a pipeline that uses a decision-tree-based classifier instead of a logistic regression.

  • The first question is to empirically evaluate whether scaling numerical features is helpful or not;

  • The second question is to evaluate whether it is empirically better (both from a computational and a statistical perspective) to use integer coded or one-hot encoded categories.

import pandas as pd

adult_census = pd.read_csv("../datasets/adult-census.csv")

target_name = "class"
target = adult_census[target_name]
data = adult_census.drop(columns=[target_name, "education-num"])

As in the previous notebooks, we use the utility make_column_selector to select only columns with a specific data type. Besides, we list in advance all categories for the categorical columns.

from sklearn.compose import make_column_selector as selector

numerical_columns_selector = selector(dtype_exclude=object)
categorical_columns_selector = selector(dtype_include=object)
numerical_columns = numerical_columns_selector(data)
categorical_columns = categorical_columns_selector(data)

Reference pipeline (no numerical scaling and integer-coded categories)#

First let’s time the pipeline we used in the main notebook to serve as a reference:

import time

from sklearn.model_selection import cross_validate
from sklearn.pipeline import make_pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.ensemble import HistGradientBoostingClassifier

categorical_preprocessor = OrdinalEncoder(
    handle_unknown="use_encoded_value", unknown_value=-1
)
preprocessor = ColumnTransformer(
    [("categorical", categorical_preprocessor, categorical_columns)],
    remainder="passthrough",
)

model = make_pipeline(preprocessor, HistGradientBoostingClassifier())

start = time.time()
cv_results = cross_validate(model, data, target)
elapsed_time = time.time() - start

scores = cv_results["test_score"]

print(
    "The mean cross-validation accuracy is: "
    f"{scores.mean():.3f} ± {scores.std():.3f} "
    f"with a fitting time of {elapsed_time:.3f}"
)

The mean cross-validation accuracy is: 0.873 ± 0.002 with a fitting time of 3.982

Scaling numerical features#

Let’s write a similar pipeline that also scales the numerical features using StandardScaler (or similar):

# Write your code here.

One-hot encoding of categorical variables#

We observed that integer coding of categorical variables can be very detrimental for linear models. However, it does not seem to be the case for HistGradientBoostingClassifier models, as the cross-validation score of the reference pipeline with OrdinalEncoder is reasonably good.

Let’s see if we can get an even better accuracy with OneHotEncoder.

Hint: HistGradientBoostingClassifier does not yet support sparse input data. You might want to use OneHotEncoder(handle_unknown="ignore", sparse_output=False) to force the use of a dense representation as a workaround.

# Write your code here.
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