View Source API Reference Scholar v0.2.1

Modules

Traditional machine learning tools built on top of Nx. Scholar implements several algorithms for classification, regression, clustering, dimensionality reduction, metrics, and preprocessing.

Model representing affinity propagation clustering. The first dimension of :clusters_centers is set to the number of samples in the dataset. The artificial centers are filled with :infinity values. To fillter them out use prune function.

Perform DBSCAN clustering from vector array or distance matrix.

Gaussian Mixture Model.

K-Means Algorithm.

Algorithms to estimate the covariance of features given a set of points.

Principal Component Analysis (PCA).

Univariate imputer for completing missing values with simple strategies.

Module for numerical integration.

Cubic Bezier Spline interpolation.

Cubic Spline interpolation.

Linear interpolation.

Ordinary least squares linear regression.

Logistic regression in both binary and multinomial variants.

Least squares polynomial regression.

Linear least squares with $L_2$ regularization.

TSNE (t-Distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality reduction technique.

Classification Metric functions.

Metrics related to clustering algorithms.

Distance metrics between multi-dimensional tensors. They all support distance calculations between any subset of axes.

Regression Metric functions.

Similarity metrics between multi-dimensional tensors.

Module containing cross validation, splitting function, and other model selection methods.

The Complement Naive Bayes classifier.

Gaussian Naive Bayes algorithm for classification.

Naive Bayes classifier for multinomial models.

The K-Nearest Neighbors. It implements both classification and regression.

The Radius Nearest Neighbors. It implements both classification and regression.

Set of functions for preprocessing data.

Statistical functions