📝 Exercise M3.01

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

Here we use a small subset of the Adult Census dataset to make the code faster 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

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"])

data_train, data_test, target_train, target_test = train_test_split(
    data, target, train_size=0.2, random_state=42
)

from sklearn.compose import ColumnTransformer
from sklearn.compose import make_column_selector as selector
from sklearn.preprocessing import OrdinalEncoder

categorical_preprocessor = OrdinalEncoder(
    handle_unknown="use_encoded_value", unknown_value=-1
)
preprocessor = ColumnTransformer(
    [
        (
            "cat_preprocessor",
            categorical_preprocessor,
            selector(dtype_include=object),
        )
    ],
    remainder="passthrough",
    sparse_threshold=0,
)

from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.pipeline import Pipeline

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

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 on the training set. We will use the following parameters search:

• learning_rate for the values 0.01, 0.1, 1 and 10. This parameter controls the ability of a new tree to correct the error of the previous sequence of trees

• max_leaf_nodes for the values 3, 10, 30. This parameter controls the depth of each tree.

# Write your code here.

Now use the test set to score the model using the best parameters that we found using cross-validation. You will have to refit the model over the full training set.

# Write your code here.