Regressors

Predicting a number (a price, a temperature, a score). BreezeML ships 24 regressors with the same compare() and one-liner API as classifiers.

Test any modelRun here

Pick a model, press Run, and it trains and cross-validates on the spot, in your browser.

Modelon: diabetes

Results will appear here.

All 24 regressors

call as breezeml.regressors.<name>(df, target). * need the [boost] extra.

Model	Call	Good for
Linear Regression	linear	The classic straight-line baseline.
Ridge	ridge	Linear + L2 penalty; resists overfitting.
Lasso	lasso	Linear + L1; also selects features (zeros them out).
Elastic Net	elastic_net	Blend of Ridge and Lasso.
Random Forest	random_forest	Robust non-linear default.
Gradient Boosting	gradient_boosting	High accuracy by correcting residuals.
Hist Gradient Boosting	hist_gradient_boosting	Fast boosting for bigger data.
Extra Trees	extra_trees	Extra-random forest, often faster.
AdaBoost	adaboost	Boosted stumps for smooth targets.
Decision Tree	decision_tree	One readable tree of rules.
K-Nearest Neighbors	knn	Averages the closest examples.
SVR	svr	Support vector regression for non-linear curves.
MLP (neural net)	mlp	Small neural network for complex shapes.
Huber	huber	Linear but robust to outliers.
Bayesian Ridge	bayesian_ridge	Ridge with uncertainty built in.
SGD	sgd	Scales to very large datasets.
Poisson	poisson	For counts (visits, calls, events).
Quantile	quantile	Predicts a percentile, not just the mean.
Theil-Sen	theilsen	Median-based, very robust to outliers.
RANSAC	ransac	Fits the inliers, ignores gross outliers.
Kernel Ridge	kernel_ridge	Ridge with a kernel for non-linearity.
Bagging	bagging	Averages many regressors to cut variance.
XGBoostextra	xgboost	Gradient boosting powerhouse.
LightGBMextra	lightgbm	Fast, light boosting.

regressors.compare(df, target) -> leaderboard

Rank all regressors on the same folds, scored by R2, MAE and RMSE.

In plain English: The same fair race, but for predicting numbers, scored by how close the guesses land.

Exampleruns locally after pip install

import breezeml

df = breezeml.datasets.diabetes()
breezeml.regressors.compare(df, "target")