331 lines
14 KiB
YAML
331 lines
14 KiB
YAML
site_name: 'Rubix ML'
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theme:
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name: material
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logo: images/app-icon-medium.png
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favicon: images/app-icon-small.png
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icon:
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repo: fontawesome/brands/github
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features:
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- navigation.tabs
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nav:
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- Home: https://rubixml.com
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- Getting Started:
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- Welcome: index.md
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- What is Machine Learning?: what-is-machine-learning.md
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- Installation: installation.md
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- Basic Introduction: basic-introduction.md
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- User Guide:
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- Representing Your Data: representing-your-data.md
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- Extracting Data: extracting-data.md
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- Preprocessing: preprocessing.md
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- Choosing an Estimator: choosing-an-estimator.md
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- Training: training.md
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- Inference: inference.md
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- Cross-validation: cross-validation.md
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- Hyper-parameter Tuning: hyper-parameter-tuning.md
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- Model Persistence: model-persistence.md
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- API Reference:
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- Fundamental Interfaces:
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- Estimator: estimator.md
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- Learner: learner.md
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- Online: online.md
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- Parallel: parallel.md
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- Persistable: persistable.md
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- Probabilistic: probabilistic.md
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- Ranks Features: ranks-features.md
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- Scoring: scoring.md
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- Verbose: verbose.md
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- Wrapper: wrapper.md
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- Extractors:
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- API Reference: extractors/api.md
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- Column Picker: extractors/column-picker.md
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- CSV: extractors/csv.md
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- JSON: extractors/json.md
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- NDJSON: extractors/ndjson.md
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- Dataset Objects:
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- API Reference: datasets/api.md
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- Generators:
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- API Reference: datasets/generators/api.md
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- Agglomerate: datasets/generators/agglomerate.md
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- Blob: datasets/generators/blob.md
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- Circle: datasets/generators/circle.md
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- Half Moon: datasets/generators/half-moon.md
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- Hyperplane: datasets/generators/hyperplane.md
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- Swiss Roll: datasets/generators/swiss-roll.md
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- Labeled: datasets/labeled.md
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- Unlabeled: datasets/unlabeled.md
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- Classifiers:
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- AdaBoost: classifiers/adaboost.md
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- Classification Tree: classifiers/classification-tree.md
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- Dummy Classifier: classifiers/dummy-classifier.md
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- Extra Tree Classifier: classifiers/extra-tree-classifier.md
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- Gaussian Naive Bayes: classifiers/gaussian-naive-bayes.md
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- K-d Neighbors: classifiers/kd-neighbors.md
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- K Nearest Neighbors: classifiers/k-nearest-neighbors.md
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- Logistic Regression: classifiers/logistic-regression.md
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- Multilayer Perceptron: classifiers/multilayer-perceptron.md
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- Naive Bayes: classifiers/naive-bayes.md
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- Radius Neighbors: classifiers/radius-neighbors.md
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- Random Forest: classifiers/random-forest.md
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- Softmax Classifier: classifiers/softmax-classifier.md
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- SVC: classifiers/svc.md
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- Regressors:
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- Adaline: regressors/adaline.md
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- Dummy Regressor: regressors/dummy-regressor.md
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- Extra Tree Regressor: regressors/extra-tree-regressor.md
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- Gradient Boost: regressors/gradient-boost.md
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- K-d Neighbors Regressor: regressors/kd-neighbors-regressor.md
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- KNN Regressor: regressors/knn-regressor.md
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- MLP Regressor: regressors/mlp-regressor.md
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- Radius Neighbors Regressor: regressors/radius-neighbors-regressor.md
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- Regression Tree: regressors/regression-tree.md
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- Ridge: regressors/ridge.md
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- SVR: regressors/svr.md
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- Clusterers:
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- Seeders:
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- K-MC2: clusterers/seeders/k-mc2.md
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- Plus Plus: clusterers/seeders/plus-plus.md
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- Random: clusterers/seeders/random.md
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- DBSCAN: clusterers/dbscan.md
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- Fuzzy C Means: clusterers/fuzzy-c-means.md
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- Gaussian Mixture: clusterers/gaussian-mixture.md
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- K Means: clusterers/k-means.md
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- Mean Shift: clusterers/mean-shift.md
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- Anomaly Detectors:
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- Gaussian MLE: anomaly-detectors/gaussian-mle.md
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- Isolation Forest: anomaly-detectors/isolation-forest.md
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- Loda: anomaly-detectors/loda.md
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- Local Outlier Factor: anomaly-detectors/local-outlier-factor.md
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- One Class SVM: anomaly-detectors/one-class-svm.md
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- Robust Z-Score: anomaly-detectors/robust-z-score.md
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- Meta Estimators:
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- Bootstrap Aggregator: bootstrap-aggregator.md
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- Committee Machine: committee-machine.md
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- Grid Search: grid-search.md
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- Persistent Model: persistent-model.md
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- Pipeline: pipeline.md
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- Embedders:
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- API Reference: embedders/api.md
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- t-SNE: embedders/t-sne.md
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- Transformers:
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- API Reference: transformers/api.md
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- Standardization and Normalization:
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- L1 Normalizer: transformers/l1-normalizer.md
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- L2 Normalizer: transformers/l2-normalizer.md
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- Max Absolute Scaler: transformers/max-absolute-scaler.md
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- Min Max Normalizer: transformers/min-max-normalizer.md
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- Robust Standardizer: transformers/robust-standardizer.md
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- Z Scale Standardizer: transformers/z-scale-standardizer.md
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- Dimensionality Reduction:
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- Dense Random Projector: transformers/dense-random-projector.md
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- Gaussian Random Projector: transformers/gaussian-random-projector.md
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- Linear Discriminant Analysis: transformers/linear-discriminant-analysis.md
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- Principal Component Analysis: transformers/principal-component-analysis.md
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- Sparse Random Projector: transformers/sparse-random-projector.md
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- Truncated SVD: transformers/truncated-svd.md
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- Feature Conversion:
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- Interval Discretizer: transformers/interval-discretizer.md
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- One Hot Encoder: transformers/one-hot-encoder.md
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- Numeric String Converter: transformers/numeric-string-converter.md
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- Boolean Converter: transformers/boolean-converter.md
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- Feature Selection:
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- K Best Feature Selector: transformers/k-best-feature-selector.md
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- Recursive Feature Eliminator: transformers/recursive-feature-eliminator.md
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- Imputation:
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- KNN Imputer: transformers/knn-imputer.md
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- Missing Data Imputer: transformers/missing-data-imputer.md
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- Random Hot Deck Imputer: transformers/random-hot-deck-imputer.md
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- Image Transformers:
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- Image Resizer: transformers/image-resizer.md
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- Image Vectorizer: transformers/image-vectorizer.md
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- Text Transformers:
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- HTML Stripper: transformers/html-stripper.md
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- Regex Filter: transformers/regex-filter.md
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- Text Normalizer: transformers/text-normalizer.md
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- Multibyte Text Normalizer: transformers/multibyte-text-normalizer.md
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- Stop Word Filter: transformers/stop-word-filter.md
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- TF-IDF Transformer: transformers/tf-idf-transformer.md
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- Whitespace Trimmer: transformers/whitespace-trimmer.md
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- Word Count Vectorizer: transformers/word-count-vectorizer.md
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- Other:
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- Polynomial Expander: transformers/polynomial-expander.md
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- Neural Network:
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- Hidden Layers:
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- Activation: neural-network/hidden-layers/activation.md
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- Batch Norm: neural-network/hidden-layers/batch-norm.md
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- Dense: neural-network/hidden-layers/dense.md
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- Dropout: neural-network/hidden-layers/dropout.md
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- Noise: neural-network/hidden-layers/noise.md
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- PReLU: neural-network/hidden-layers/prelu.md
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- Activation Functions:
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- ELU: neural-network/activation-functions/elu.md
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- Hyperbolic Tangent: neural-network/activation-functions/hyperbolic-tangent.md
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- Leaky ReLU: neural-network/activation-functions/leaky-relu.md
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- ReLU: neural-network/activation-functions/relu.md
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- SELU: neural-network/activation-functions/selu.md
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- Sigmoid: neural-network/activation-functions/sigmoid.md
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- Softmax: neural-network/activation-functions/softmax.md
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- Soft Plus: neural-network/activation-functions/soft-plus.md
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- Soft Sign: neural-network/activation-functions/softsign.md
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- Thresholded ReLU: neural-network/activation-functions/thresholded-relu.md
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- Cost Functions:
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- Cross Entropy: neural-network/cost-functions/cross-entropy.md
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- Huber Loss: neural-network/cost-functions/huber-loss.md
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- Least Squares: neural-network/cost-functions/least-squares.md
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- Relative Entropy: neural-network/cost-functions/relative-entropy.md
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- Initializers:
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- Constant: neural-network/initializers/constant.md
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- He: neural-network/initializers/he.md
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- LeCun: neural-network/initializers/lecun.md
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- Normal: neural-network/initializers/normal.md
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- Uniform: neural-network/initializers/uniform.md
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- Xavier 1: neural-network/initializers/xavier-1.md
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- Xavier 2: neural-network/initializers/xavier-2.md
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- Optimizers:
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- AdaGrad: neural-network/optimizers/adagrad.md
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- Adam: neural-network/optimizers/adam.md
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- AdaMax: neural-network/optimizers/adamax.md
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- Cyclical: neural-network/optimizers/cyclical.md
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- Momentum: neural-network/optimizers/momentum.md
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- RMS Prop: neural-network/optimizers/rms-prop.md
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- Step Decay: neural-network/optimizers/step-decay.md
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- Stochastic: neural-network/optimizers/stochastic.md
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- Graph:
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- Trees:
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- Ball Tree: graph/trees/ball-tree.md
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- K-d Tree: graph/trees/k-d-tree.md
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- Kernels:
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- Distance:
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- Canberra: kernels/distance/canberra.md
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- Cosine: kernels/distance/cosine.md
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- Diagonal: kernels/distance/diagonal.md
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- Euclidean: kernels/distance/euclidean.md
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- Hamming: kernels/distance/hamming.md
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- Jaccard: kernels/distance/jaccard.md
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- Manhattan: kernels/distance/manhattan.md
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- Minkowski: kernels/distance/minkowski.md
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- Safe Euclidean: kernels/distance/safe-euclidean.md
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- SVM:
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- Linear: kernels/svm/linear.md
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- Polynomial: kernels/svm/polynomial.md
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- RBF: kernels/svm/rbf.md
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- Sigmoidal: kernels/svm/sigmoidal.md
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- Cross Validation:
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- Metrics:
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- API Reference: cross-validation/metrics/api.md
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- Accuracy: cross-validation/metrics/accuracy.md
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- F Beta: cross-validation/metrics/f-beta.md
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- Informedness: cross-validation/metrics/informedness.md
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- MCC: cross-validation/metrics/mcc.md
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- Mean Absolute Error: cross-validation/metrics/mean-absolute-error.md
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- Mean Squared Error: cross-validation/metrics/mean-squared-error.md
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- Median Absolute Error: cross-validation/metrics/median-absolute-error.md
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- RMSE: cross-validation/metrics/rmse.md
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- R Squared: cross-validation/metrics/r-squared.md
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- SMAPE: cross-validation/metrics/smape.md
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- Completeness: cross-validation/metrics/completeness.md
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- Homogeneity: cross-validation/metrics/homogeneity.md
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- Rand Index: cross-validation/metrics/rand-index.md
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- V Measure: cross-validation/metrics/v-measure.md
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- Reports:
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- API Reference: cross-validation/reports/api.md
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- Aggregate Report: cross-validation/reports/aggregate-report.md
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- Confusion Matrix: cross-validation/reports/confusion-matrix.md
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- Contingency Table: cross-validation/reports/contingency-table.md
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- Error Analysis: cross-validation/reports/error-analysis.md
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- Multiclass Breakdown: cross-validation/reports/multiclass-breakdown.md
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- Validators:
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- API Reference: cross-validation/api.md
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- Hold Out: cross-validation/hold-out.md
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- K Fold: cross-validation/k-fold.md
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- Leave P Out: cross-validation/leave-p-out.md
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- Monte Carlo: cross-validation/monte-carlo.md
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- Persisters:
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- API Reference: persisters/api.md
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- Filesystem: persisters/filesystem.md
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- Flysystem: persisters/flysystem.md
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- Redis DB: persisters/redis-db.md
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- Serializers:
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- Gzip: persisters/serializers/gzip.md
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- Igbinary: persisters/serializers/igbinary.md
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- Native: persisters/serializers/native.md
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- RBX: persisters/serializers/rbx.md
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- Backends:
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- Amp: backends/amp.md
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- Serial: backends/serial.md
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- Other:
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- Helpers:
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- Params: other/helpers/params.md
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- Loggers:
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- Screen: other/loggers/screen.md
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- Strategies:
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- Constant: other/strategies/constant.md
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- K Most Frequent: other/strategies/k-most-frequent.md
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- Mean: other/strategies/mean.md
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- Percentile: other/strategies/percentile.md
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- Prior: other/strategies/prior.md
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- Wild Guess: other/strategies/wild-guess.md
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- Tokenizers:
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- K-Skip-N-Gram: other/tokenizers/k-skip-n-gram.md
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- N-Gram: other/tokenizers/n-gram.md
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- Sentence: other/tokenizers/sentence.md
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- Skip Gram: other/tokenizers/skip-gram.md
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- Whitespace: other/tokenizers/whitespace.md
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- Word: other/tokenizers/word.md
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- FAQ: faq.md
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extra:
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version:
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provider: mike
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social:
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- icon: fontawesome/brands/github
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link: https://github.com/RubixML
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- icon: fontawesome/brands/telegram
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link: https://t.me/RubixML
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- icon: fontawesome/brands/twitter
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link: https://twitter.com/RubixML
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- icon: fontawesome/brands/linkedin
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link: https://www.linkedin.com/groups/8952251/
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use_directory_urls: false
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plugins:
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- search
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- git-revision-date
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markdown_extensions:
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- attr_list
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- abbr
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- admonition
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- pymdownx.highlight:
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extend_pygments_lang:
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- name: php
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lang: php
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options:
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startinline: true
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- pymdownx.superfences
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- pymdownx.arithmatex:
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generic: true
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- toc:
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permalink: "#"
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- footnotes
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google_analytics:
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- UA-136137674-1
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- auto
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extra_javascript:
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- https://polyfill.io/v3/polyfill.min.js?features=es6
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- https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
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- js/custom.js
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extra_css:
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- css/custom.css
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repo_url: https://github.com/RubixML/ML
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site_url: https://rubixml.com
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site_description: 'A high-level machine learning and deep learning library for the PHP language.'
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copyright: '© 2021 Rubix ML'
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