Default Regression Algorithms

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

Note

The default algorithms serve as a convenience for getting started with regression 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.

Regression Algorithms

Algorithm	Internal Name	Hyperparameter Grid
Linear Regression	linear
Ridge Regression	ridge	alpha: logarithmic space from 10^-3 to 10^0 (100 values)
LASSO Regression	lasso	alpha: logarithmic space from 10^-3 to 10^0 (100 values)
Bayesian Ridge Regression	bridge	alpha_1: [1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1] alpha_2: [1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1] lambda_1: [1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1] lambda_2: [1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1]
Elastic Net Regression	elasticnet	alpha: logarithmic space from 10^-3 to 10^0 (100 values) l1_ratio: 0.1 to 0.9 (step 0.1)
Decision Tree Regression	dtr	criterion: [“friedman_mse”, “absolute_error”, “poisson”, “squared_error”] max_depth: [5, 10, 15, 20, None]
Random Forest	rf	n_estimators: 20 to 140 (step 20) criterion: [“friedman_mse”, “absolute_error”, “poisson”, “squared_error”] max_depth: [5, 10, 15, 20, None]
Support Vector Regression	svr	kernel: [“linear”, “rbf”, “sigmoid”] C: 1 to 29.5 (step 0.5) gamma: [“scale”, “auto”, 0.001, 0.01, 0.1]
Multi-Layer Perceptron Regression	mlp	hidden_layer_sizes: [(100,), (50, 25), (25, 10), (100, 50, 25), (50, 25, 10)] activation: [“identity”, “logistic”, “tanh”, “relu”] alpha: [0.0001, 0.001, 0.01] learning_rate: [“constant”, “invscaling”, “adaptive”]
K-Nearest Neighbour Regression	knn	n_neighbors: [1, 3] weights: [“uniform”, “distance”] algorithm: [“auto”, “ball_tree”, “kd_tree”, “brute”] leaf_size: 5 to 45 (step 5)

Usage

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

from brisk import REGRESSION_ALGORITHMS

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

# Or select specific algorithms
ALGORITHM_CONFIG = brisk.AlgorithmCollection(
    REGRESSION_ALGORITHMS[0],  # linear regression
    REGRESSION_ALGORITHMS[1],  # ridge regression
)