zero-one.geni.ml.classification — zero.one/geni 0.0.39

decision-tree-classifier clj

(decision-tree-classifier params)

Decision tree learning algorithm (http://en.wikipedia.org/wiki/Decision_tree_learning) for classification. It supports both binary and multiclass labels, as well as both continuous and categorical features.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.html

Timestamp: 2020-10-19T01:55:55.948Z

fm-classifier clj

(fm-classifier params)

Factorization Machines learning algorithm for classification. It supports normal gradient descent and AdamW solver.

The implementation is based upon:

S. Rendle. "Factorization machines" 2010.

FM is able to estimate interactions even in problems with huge sparsity (like advertising and recommendation system). FM formula is:

FM classification model uses logistic loss which can be solved by gradient descent method, and regularization terms like L2 are usually added to the loss function to prevent overfitting.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/classification/FMClassifier.html

Timestamp: 2020-10-19T01:55:56.340Z

gbt-classifier clj

(gbt-classifier params)

Gradient-Boosted Trees (GBTs) (http://en.wikipedia.org/wiki/Gradient_boosting) learning algorithm for classification. It supports binary labels, as well as both continuous and categorical features.

The implementation is based upon: J.H. Friedman. "Stochastic Gradient Boosting." 1999.

Notes on Gradient Boosting vs. TreeBoost:

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/classification/GBTClassifier.html

Timestamp: 2020-10-19T01:55:56.899Z

linear-svc clj

(linear-svc params)

Linear SVM Classifier

This binary classifier optimizes the Hinge Loss using the OWLQN optimizer. Only supports L2 regularization currently.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/classification/LinearSVC.html

Timestamp: 2020-10-19T01:55:57.279Z

logistic-regression clj

(logistic-regression params)

Logistic regression. Supports:

This class supports fitting traditional logistic regression model by LBFGS/OWLQN and bound (box) constrained logistic regression model by LBFGSB.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/classification/LogisticRegression.html

Timestamp: 2020-10-19T01:55:57.830Z

mlp-classifier clj

(mlp-classifier params)

Classifier trainer based on the Multilayer Perceptron. Each layer has sigmoid activation function, output layer has softmax. Number of inputs has to be equal to the size of feature vectors. Number of outputs has to be equal to the total number of labels.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.html

Timestamp: 2020-10-19T01:55:58.225Z

multilayer-perceptron-classifier clj

(multilayer-perceptron-classifier params)

Classifier trainer based on the Multilayer Perceptron. Each layer has sigmoid activation function, output layer has softmax. Number of inputs has to be equal to the size of feature vectors. Number of outputs has to be equal to the total number of labels.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.html

Timestamp: 2020-10-19T01:55:58.225Z

naive-bayes clj

(naive-bayes params)

Naive Bayes Classifiers. It supports Multinomial NB (see here) which can handle finitely supported discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification. By making every vector a binary (0/1) data, it can also be used as Bernoulli NB (see here). The input feature values for Multinomial NB and Bernoulli NB must be nonnegative. Since 3.0.0, it supports Complement NB which is an adaptation of the Multinomial NB. Specifically, Complement NB uses statistics from the complement of each class to compute the model's coefficients The inventors of Complement NB show empirically that the parameter estimates for CNB are more stable than those for Multinomial NB. Like Multinomial NB, the input feature values for Complement NB must be nonnegative. Since 3.0.0, it also supports Gaussian NB (see here) which can handle continuous data.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/classification/NaiveBayes.html

Timestamp: 2020-10-19T01:55:58.596Z

one-vs-rest clj

(one-vs-rest params)

Reduction of Multiclass Classification to Binary Classification. Performs reduction using one against all strategy. For a multiclass classification with k classes, train k models (one per class). Each example is scored against all k models and the model with highest score is picked to label the example.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/classification/OneVsRest.html

Timestamp: 2020-10-19T01:55:58.960Z

random-forest-classifier clj

(random-forest-classifier params)

Random Forest learning algorithm for classification. It supports both binary and multiclass labels, as well as both continuous and categorical features.

Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/classification/RandomForestClassifier.html

Timestamp: 2020-10-19T01:55:59.351Z