Default Classification Algorithms

Brisk provides a set of predefined classification algorithms wrapped as AlgorithmWrapper instances. These algorithms are sourced from scikit-learn.

Note

The default algorithms serve as a convenience for getting started with classification tasks but are unlikely to be optimal for most projects. You should consider these as a starting point you can use to get familiar with the framework. There are many other algorithms available in scikit-learn and many more hyperparameters you may want to consider.

Classification Algorithms

Algorithm	Internal Name	Hyperparameter Grid
Logistic Regression	logistic	penalty: [None, “l2”, “l1”, “elasticnet”] l1_ratio: 0.1 to 0.9 (step 0.1) C: 1 to 29.5 (step 0.5)
Support Vector Classification	svc	kernel: [“linear”, “rbf”, “sigmoid”] C: 1 to 29.5 (step 0.5) gamma: [“scale”, “auto”, 0.001, 0.01, 0.1]
k-Nearest Neighbours Classifier	knn_classifier	n_neighbors: [1, 3] weights: [“uniform”, “distance”] algorithm: [“auto”, “ball_tree”, “kd_tree”, “brute”] leaf_size: 5 to 45 (step 5)
Decision Tree Classifier	dtc	criterion: [“gini”, “entropy”, “log_loss”] max_depth: [5, 10, 15, 20, None]
Random Forest Classifier	rf_classifier	n_estimators: 20 to 140 (step 20) criterion: [“friedman_mse”, “absolute_error”, “poisson”, “squared_error”] max_depth: [5, 10, 15, 20, None]
Gaussian Naive Bayes	gaussian_nb	var_smoothing: [1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4]
Ridge Classifier	ridge_classifier	alpha: logarithmic space from 10^-3 to 10^0 (100 values)
Bagging Classifier	bagging_classifier	n_estimators: 10 to 150 (step 20)
Voting Classifier	voting_classifier	voting: [“hard”, “soft”]

Usage

To use these algorithms in your Brisk project, you can import them directly:

from brisk import CLASSIFICATION_ALGORITHMS

# In your algorithms.py file
ALGORITHM_CONFIG = brisk.AlgorithmCollection(
    *CLASSIFICATION_ALGORITHMS
)

# Or select specific algorithms
ALGORITHM_CONFIG = brisk.AlgorithmCollection(
    CLASSIFICATION_ALGORITHMS[0],  # logistic regression
    CLASSIFICATION_ALGORITHMS[3],  # decision tree
)