Products
In-IDE
IDE extension that lets you fix coding issues before they exist!
Discover SonarLint
SaaS
Setup is effortless and analysis is automatic for most languages
Discover SonarCloud
Self-Hosted
Fast, accurate analysis; enterprise scalability
Discover SonarQube

Python static code analysis

Unique rules to find Bugs, Vulnerabilities, Security Hotspots, and Code Smells in your PYTHON code

Important hyperparameters should be specified for machine learning libraries' estimators and optimizers

consistency - conventional

maintainability

reliability

Code Smell

MajorSonarSource default severity
click to learn more

This rule raises an issue when a machine learning estimator or optimizer is instantiated without specifying the important hyperparameters.

Why is this an issue?

How can I fix it?

More Info

When instantiating an estimator or an optimizer, default values for any hyperparameters that are not specified will be used. Relying on the default values can lead to non-reproducible results across different 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 :

Scikit-learn - 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

PyTorch - Optimizer	Hyperparameters
Adadelta	lr, weight_decay
Adagrad	lr, weight_decay
Adam	lr, weight_decay
AdamW	lr, weight_decay
SparseAdam	lr
Adamax	lr, weight_decay
ASGD	lr, weight_decay
LBFGS	lr
NAdam	lr, weight_decay, momentum_decay
RAdam	lr, weight_decay
RMSprop	lr, weight_decay, momentum
Rprop	lr
SGD	lr, weight_decay, momentum

Available In:

Catch issues on the fly,
in your IDE

Detect issues in your GitHub, Azure DevOps Services, Bitbucket Cloud, GitLab repositories

Analyze code in your
on-premise CI

In-IDE

SaaS

Self-Hosted