Predicting a category (spam or not, which species, which class). BreezeML ships 24 classifiers. Use classifiers.compare() to rank them all on your data, or call any one directly. Each returns a fitted model plus an honest cross-validated report.
Pick a model, press Run, and it trains and cross-validates on the spot, in your browser.
Results will appear here.
call as breezeml.classifiers.<name>(df, target). Marked * need the optional [boost] extra and run locally, not in the browser.
| Model | Call | Good for |
|---|---|---|
| Logistic Regression | logistic | Fast, interpretable baseline. Always try this first. |
| Random Forest | random_forest | Robust all-rounder. Great default on tabular data. |
| Gradient Boosting | gradient_boosting | Strong accuracy by correcting its own mistakes. |
| Hist Gradient Boosting | hist_gradient_boosting | Fast boosting for larger datasets. |
| Extra Trees | extra_trees | Like random forest, more randomness, often faster. |
| AdaBoost | adaboost | Boosts weak learners; good on clean data. |
| Decision Tree | decision_tree | One readable tree of if/else rules. |
| K-Nearest Neighbors | knn | Predicts from the closest examples. No training. |
| SVM (RBF) | svm | Powerful non-linear boundaries on small/medium data. |
| Linear SVM | linear_svm | Fast linear margin classifier for many features. |
| MLP (neural net) | mlp | A small neural network for non-linear patterns. |
| Ridge Classifier | ridge | Regularized linear classifier, resists overfitting. |
| SGD Classifier | sgd | Scales to huge datasets via online learning. |
| Passive Aggressive | passive_aggressive | Online learner for streaming text/data. |
| LDA | lda | Linear discriminant analysis, elegant on Gaussian data. |
| QDA | qda | Quadratic version, curved boundaries. |
| Gaussian NB | gaussian_nb | Naive Bayes for continuous features. Very fast. |
| Multinomial NB | multinomial_nb | Naive Bayes for counts, classic for text. |
| Complement NB | complement_nb | Naive Bayes tuned for imbalanced text. |
| Bernoulli NB | bernoulli_nb | Naive Bayes for binary features. |
| Nearest Centroid | nearest_centroid | Assigns to the closest class average. Tiny + fast. |
| Bagging | bagging | Averages many models to cut variance. |
| XGBoostextra | xgboost | Kaggle-winning gradient boosting. |
| LightGBMextra | lightgbm | Very fast, memory-light boosting. |
classifiers.compare(df, target) -> leaderboardRun hereThe signature move: rank every classifier on the SAME cross-validation folds, then light the winner. Fair by construction. (The browser runs a fast subset; run locally for all 24.)
In plain English: A fair race: every model runs on the exact same splits, and the real winner lights up. No cherry-picking.
Press Run to execute this snippet.
confusion matrixRun hereA confusion matrix shows, for every true class, what the model guessed. Bright squares on the diagonal are correct; anything off-diagonal is a mix-up. Press Run to see one for real wine classes.
In plain English: A grid of right versus wrong. The bright diagonal is what it got correct; off-diagonal squares show which classes it mixes up.
Press Run to execute this snippet.
classifiers.random_forest(df, target) -> (model, report)Every classifier is a one-liner that returns a fitted model and an honest report. Swap the name to swap the algorithm.
In plain English: Prefer a specific algorithm? Every one is a single line that trains it and grades it. Change the name, change the method.
import breezeml
df = breezeml.datasets.iris()
model, report = breezeml.classifiers.random_forest(df, "species")
print(report)classifiers.quick_tune(df, target, algo='random_forest')Tune the winner's hyperparameters over a small, sensible grid, no sprawling search.
In plain English: Nudges the winner's settings over a small, sensible range to squeeze out a little more, without a giant search.
import breezeml
df = breezeml.datasets.wine()
best = breezeml.classifiers.quick_tune(df, "wine_class", algo="random_forest")