Exercise 02¶
The goal of this exercise is to evalutate the impact of using an arbitrary integer encoding for categorical variables along with a linear classification model such as Logistic Regression.
To do so, let’s try to use OrdinalEncoder to preprocess the categorical
variables. This preprocessor is assembled in a pipeline with
LogisticRegression. The performance of the pipeline can be evaluated as
usual by cross-validation and then compared to the score obtained when using
OneHotEncoding or to some other baseline score.
Because OrdinalEncoder can raise errors if it sees an unknown category at
prediction time, we need to pre-compute the list of all possible categories
ahead of time:
categories = [data[column].unique()
for column in data[categorical_columns]]
OrdinalEncoder(categories=categories)
import pandas as pd
df = pd.read_csv("../datasets/adult-census.csv")
target_name = "class"
target = df[target_name].to_numpy()
data = df.drop(columns=[target_name, "fnlwgt"])
from sklearn.compose import make_column_selector as selector
categorical_columns_selector = selector(dtype_exclude=["int", "float"])
categorical_columns = categorical_columns_selector(data)
data_categorical = data[categorical_columns]
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OrdinalEncoder
from sklearn.linear_model import LogisticRegression
# TODO: write me!