Set and get hyperparameters in scikit-learn#

The process of learning a predictive model is driven by a set of internal parameters and a set of training data. These internal parameters are called hyperparameters and are specific for each family of models. In addition, a specific set of hyperparameters are optimal for a specific dataset and thus they need to be optimized.


In this notebook we will use the words β€œhyperparameters” and β€œparameters” interchangeably.

This notebook shows how one can get and set the value of a hyperparameter in a scikit-learn estimator. We recall that hyperparameters refer to the parameter that will control the learning process.

They should not be confused with the fitted parameters, resulting from the training. These fitted parameters are recognizable in scikit-learn because they are spelled with a final underscore _, for instance model.coef_.

We will start by loading the adult census dataset and only use the numerical features.

import pandas as pd

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

target_name = "class"
numerical_columns = ["age", "capital-gain", "capital-loss", "hours-per-week"]

target = adult_census[target_name]
data = adult_census[numerical_columns]

Our data is only numerical.

age capital-gain capital-loss hours-per-week
0 25 0 0 40
1 38 0 0 50
2 28 0 0 40
3 44 7688 0 40
4 18 0 0 30

Let’s create a simple predictive model made of a scaler followed by a logistic regression classifier.

As mentioned in previous notebooks, many models, including linear ones, work better if all features have a similar scaling. For this purpose, we use a StandardScaler, which transforms the data by rescaling features.

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

model = Pipeline(
        ("preprocessor", StandardScaler()),
        ("classifier", LogisticRegression()),

We can evaluate the generalization performance of the model via cross-validation.

from sklearn.model_selection import cross_validate

cv_results = cross_validate(model, data, target)
scores = cv_results["test_score"]
    "Accuracy score via cross-validation:\n"
    f"{scores.mean():.3f} Β± {scores.std():.3f}"
Accuracy score via cross-validation:
0.800 Β± 0.003

We created a model with the default C value that is equal to 1. If we wanted to use a different C parameter we could have done so when we created the LogisticRegression object with something like LogisticRegression(C=1e-3).


For more information on the model hyperparameter C, refer to the documentation. Be aware that we will focus on linear models in an upcoming module.

We can also change the parameter of a model after it has been created with the set_params method, which is available for all scikit-learn estimators. For example, we can set C=1e-3, fit and evaluate the model:

cv_results = cross_validate(model, data, target)
scores = cv_results["test_score"]
    "Accuracy score via cross-validation:\n"
    f"{scores.mean():.3f} Β± {scores.std():.3f}"
Accuracy score via cross-validation:
0.787 Β± 0.002

When the model of interest is a Pipeline, the parameter names are of the form <model_name>__<parameter_name> (note the double underscore in the middle). In our case, classifier comes from the Pipeline definition and C is the parameter name of LogisticRegression.

In general, you can use the get_params method on scikit-learn models to list all the parameters with their values. For example, if you want to get all the parameter names, you can use:

for parameter in model.get_params():

.get_params() returns a dict whose keys are the parameter names and whose values are the parameter values. If you want to get the value of a single parameter, for example classifier__C, you can use:


We can systematically vary the value of C to see if there is an optimal value.

for C in [1e-3, 1e-2, 1e-1, 1, 10]:
    cv_results = cross_validate(model, data, target)
    scores = cv_results["test_score"]
        f"Accuracy score via cross-validation with C={C}:\n"
        f"{scores.mean():.3f} Β± {scores.std():.3f}"
Accuracy score via cross-validation with C=0.001:
0.787 Β± 0.002
Accuracy score via cross-validation with C=0.01:
0.799 Β± 0.003
Accuracy score via cross-validation with C=0.1:
0.800 Β± 0.003
Accuracy score via cross-validation with C=1:
0.800 Β± 0.003
Accuracy score via cross-validation with C=10:
0.800 Β± 0.003

We can see that as long as C is high enough, the model seems to perform well.

What we did here is very manual: it involves scanning the values for C and picking the best one manually. In the next lesson, we will see how to do this automatically.


When we evaluate a family of models on test data and pick the best performer, we can not trust the corresponding prediction accuracy, and we need to apply the selected model to new data. Indeed, the test data has been used to select the model, and it is thus no longer independent from this model.

In this notebook we have seen:

  • how to use get_params and set_params to get the parameters of a model and set them.