This rule raises an issue when a Scikit-learn model is instantiated without specifying the important hyperparameters.

Why is this an issue?

When instantiating a Scikit-learn estimator, it will use default values for the hyperparameters that are not specified. Relying on the default values can lead to non-reproducible results across diffferent versions of the library.

Furthermore, the default values might not be the best choice for the specific problem at hand and can lead to suboptimal performance.

Here are the estimators and the parameters considered by this rule :

Estimator	Hyperparameters
AdaBoostClassifier	learning_rate
AdaBoostRegressor	learning_rate
GradientBoostingClassifier	learning_rate
GradientBoostingRegressor	learning_rate
HistGradientBoostingClassifier	learning_rate
HistGradientBoostingRegressor	learning_rate
RandomForestClassifier	min_samples_leaf, max_features
RandomForestRegressor	min_samples_leaf, max_features
ElasticNet	alpha, l1_ratio
NearestNeighbors	n_neighbors
KNeighborsClassifier	n_neighbors
KNeighborsRegressor	n_neighbors
NuSVC	nu, kernel, gamma
NuSVR	C, kernel, gamma
SVC	C, kernel, gamma
SVR	C, kernel, gamma
DecisionTreeClassifier	ccp_alpha
DecisionTreeRegressor	ccp_alpha
MLPClassifier	hidden_layer_sizes
MLPRegressor	hidden_layer_sizes
PolynomialFeatures	degree, interaction_only

How to fix it

Specify the hyperparameters when instantiating the estimator.

Code examples

Noncompliant code example

from sklearn.neighbors import KNeighborsClassifier

clf = KNeighborsClassifier() # Noncompliant : n_neighbors is not specified, different values can change the behaviour of the predictor significantly

Compliant solution

from sklearn.neighbors import KNeighborsClassifier

clf = KNeighborsClassifier( # Compliant
    n_neighbors=5
)

Resources

Articles & blog posts

Probst, P., Boulesteix, A. L., & Bischl, B. (2019). Tunability: Importance of Hyperparameters of Machine Learning Algorithms. Journal of Machine Learning Research, 20(53), 1-32.
van Rijn, J. N., & Hutter, F. (2018, July). Hyperparameter importance across datasets. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2367-2376).

External coding guidelines

Code Smells for Machine Learning Applications - Hyperparameter not Explicitly Set