📝 Exercise M3.01
📝 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 treesmax_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 in the training set.
# Write your code here.