Exercise 01¶

The goal is to write an exhaustive search to find the best parameters combination maximizing the model performance.

Here we use a small subset of the Adult Census dataset to make to code fast to execute. Once your code works on the small subset, try to change train_size to a larger value (e.g. 0.8 for 80% instead of 20%).

import pandas as pd

from sklearn.model_selection import train_test_split

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

target_name = "class"
target = df[target_name].to_numpy()
data = df.drop(columns=[target_name, "fnlwgt"])

df_train, df_test, target_train, target_test = train_test_split(
    data, target, train_size=0.2, random_state=42)

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OrdinalEncoder

categorical_columns = [
    'workclass', 'education', 'marital-status', 'occupation',
    'relationship', 'race', 'native-country', 'sex']

categories = [data[column].unique()
              for column in data[categorical_columns]]

categorical_preprocessor = OrdinalEncoder(categories=categories)

preprocessor = ColumnTransformer(
    [('cat-preprocessor', categorical_preprocessor, categorical_columns)],
    remainder='passthrough', sparse_threshold=0)

# This line is currently required to import HistGradientBoostingClassifier
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.pipeline import Pipeline

model = Pipeline([
    ("preprocessor", preprocessor),
    ("classifier", HistGradientBoostingClassifier(random_state=42))
])

TODO: write your solution below

Use the previously defined model (called model) and using two nested for loops, make a search of the best combinations of the learning_rate and max_leaf_nodes parameters. In this regard, you will need to train and test the model by setting the parameters. The evaluation of the model should be performed using cross_val_score. We can propose to define the following parameters search:

learning_rate for the values 0.01, 0.1, 1 and 10
max_leaf_nodes for the values 3, 10, 30

Scikit-learn tutorial

Exercise 01¶