# Scholar v0.4.1 - Table of Contents

## Guides

- [README](readme.md)
- [Cross-validation with gradient boosting trees](cv_gradient_boosting_tree.md)
- [k-means clustering](k_means.md)
- [k-nearest neighbors (KNN)](k_nearest_neighbors.md)
- [Linear regression in practice](linear_regression.md)
- [Manifold learning](manifold_learning.md)
- [Multidimensional scaling (MDS)](mds.md)
- [Nearest neighbors](nearest_neighbors.md)

## Modules

- [Scholar.Cluster.OPTICS](Scholar.Cluster.OPTICS.md): OPTICS (Ordering Points To Identify the Clustering Structure) is an algorithm
for finding density-based clusters in spatial data.
- [Scholar.Covariance.LedoitWolf](Scholar.Covariance.LedoitWolf.md): Ledoit-Wolf is a particular form of shrinkage covariance estimator,
where the shrinkage coefficient is computed using O. Ledoit and M. Wolf’s formula.
- [Scholar.Covariance.ShrunkCovariance](Scholar.Covariance.ShrunkCovariance.md): Covariance estimator with shrinkage.

- [Scholar.CrossDecomposition.PLSSVD](Scholar.CrossDecomposition.PLSSVD.md): Partial Least Square SVD.
- [Scholar.Decomposition.TruncatedSVD](Scholar.Decomposition.TruncatedSVD.md): Dimensionality reduction using truncated SVD (aka LSA).
- [Scholar.FeatureExtraction.CountVectorizer](Scholar.FeatureExtraction.CountVectorizer.md): A `CountVectorizer` converts already indexed collection of text documents to a matrix of token counts.

- [Scholar.Impute.KNNImputter](Scholar.Impute.KNNImputter.md): Imputer for completing missing values using k-Nearest Neighbors.
- [Scholar.NaiveBayes.Bernoulli](Scholar.NaiveBayes.Bernoulli.md): Naive Bayes classifier for multivariate Bernoulli models.
- [Scholar.NaiveBayes.Categorical](Scholar.NaiveBayes.Categorical.md): Naive Bayes classifier for categorical features.
- [Scholar.Optimize.Brent](Scholar.Optimize.Brent.md): Brent's method for univariate function minimization.
- [Scholar.Optimize.GoldenSection](Scholar.Optimize.GoldenSection.md): Golden section search for univariate function minimization.
- [Scholar.Preprocessing.Binarizer](Scholar.Preprocessing.Binarizer.md): Binarize data according to a threshold.

- [Scholar.Preprocessing.RobustScaler](Scholar.Preprocessing.RobustScaler.md): Scale features using statistics that are robust to outliers.

- Models
  - [Scholar.Cluster.AffinityPropagation](Scholar.Cluster.AffinityPropagation.md): Model representing affinity propagation clustering. The first dimension
of `:cluster_centers` is set to the number of samples in the dataset.
The artificial centers are filled with `:infinity` values. To filter
them out use the `prune` function.
  - [Scholar.Cluster.DBSCAN](Scholar.Cluster.DBSCAN.md): Perform DBSCAN clustering from vector array or distance matrix.
  - [Scholar.Cluster.GaussianMixture](Scholar.Cluster.GaussianMixture.md): Gaussian Mixture Model.
  - [Scholar.Cluster.Hierarchical](Scholar.Cluster.Hierarchical.md): Performs [hierarchical, agglomerative clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering#Agglomerative_clustering_example)
on a dataset.
  - [Scholar.Cluster.KMeans](Scholar.Cluster.KMeans.md): K-Means Algorithm.
  - [Scholar.Decomposition.PCA](Scholar.Decomposition.PCA.md): Principal Component Analysis (PCA).
  - [Scholar.Integrate](Scholar.Integrate.md): Module for numerical integration.

  - [Scholar.Interpolation.BezierSpline](Scholar.Interpolation.BezierSpline.md): Cubic Bezier Spline interpolation.
  - [Scholar.Interpolation.CubicSpline](Scholar.Interpolation.CubicSpline.md): Cubic Spline interpolation.
  - [Scholar.Interpolation.Linear](Scholar.Interpolation.Linear.md): Linear interpolation.
  - [Scholar.Linear.BayesianRidgeRegression](Scholar.Linear.BayesianRidgeRegression.md): Bayesian ridge regression: A fully probabilistic linear model with parameter regularization.
  - [Scholar.Linear.IsotonicRegression](Scholar.Linear.IsotonicRegression.md): Isotonic regression is a method of fitting a free-form line to a set of
observations by solving a convex optimization problem. It is a form of
regression analysis that can be used as an alternative to polynomial
regression to fit nonlinear data.
  - [Scholar.Linear.LinearRegression](Scholar.Linear.LinearRegression.md): Ordinary least squares linear regression.
  - [Scholar.Linear.LogisticRegression](Scholar.Linear.LogisticRegression.md): Multiclass logistic regression.
  - [Scholar.Linear.PolynomialRegression](Scholar.Linear.PolynomialRegression.md): Least squares polynomial regression.
  - [Scholar.Linear.RidgeRegression](Scholar.Linear.RidgeRegression.md): Linear least squares with $L_2$ regularization.
  - [Scholar.Linear.SVM](Scholar.Linear.SVM.md): Support Vector Machine linear classifier.
  - [Scholar.Manifold.MDS](Scholar.Manifold.MDS.md): Multidimensional scaling (MDS) seeks a low-dimensional representation of the data
in which the distances respect well the distances in the original high-dimensional space.

  - [Scholar.Manifold.TSNE](Scholar.Manifold.TSNE.md): t-SNE (t-Distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality reduction technique.
  - [Scholar.Manifold.Trimap](Scholar.Manifold.Trimap.md): TriMap: Large-scale Dimensionality Reduction Using Triplets.
  - [Scholar.NaiveBayes.Complement](Scholar.NaiveBayes.Complement.md): The Complement Naive Bayes classifier.
  - [Scholar.NaiveBayes.Gaussian](Scholar.NaiveBayes.Gaussian.md): Gaussian Naive Bayes algorithm for classification.
  - [Scholar.NaiveBayes.Multinomial](Scholar.NaiveBayes.Multinomial.md): Naive Bayes classifier for multinomial models.
  - [Scholar.Neighbors.BruteKNN](Scholar.Neighbors.BruteKNN.md): Brute-Force k-Nearest Neighbor Search Algorithm.
  - [Scholar.Neighbors.KDTree](Scholar.Neighbors.KDTree.md): Implements a k-d tree, a space-partitioning data structure for organizing points
in a k-dimensional space.
  - [Scholar.Neighbors.KNNClassifier](Scholar.Neighbors.KNNClassifier.md): K-Nearest Neighbors Classifier.
  - [Scholar.Neighbors.KNNRegressor](Scholar.Neighbors.KNNRegressor.md): K-Nearest Neighbors Regressor.
  - [Scholar.Neighbors.LargeVis](Scholar.Neighbors.LargeVis.md): LargeVis algorithm for approximate k-nearest neighbor (k-NN) graph construction.
  - [Scholar.Neighbors.NNDescent](Scholar.Neighbors.NNDescent.md): Nearest Neighbors Descent (NND) is an algorithm that calculates Approximated Nearest Neighbors (ANN)
for a given set of points[1].
  - [Scholar.Neighbors.RadiusNNClassifier](Scholar.Neighbors.RadiusNNClassifier.md): The Radius Nearest Neighbors.
  - [Scholar.Neighbors.RadiusNNRegressor](Scholar.Neighbors.RadiusNNRegressor.md): The Radius Nearest Neighbors.
  - [Scholar.Neighbors.RandomProjectionForest](Scholar.Neighbors.RandomProjectionForest.md): Random Projection Forest for k-Nearest Neighbor Search.

- Utilities
  - [Scholar.Impute.SimpleImputer](Scholar.Impute.SimpleImputer.md): Univariate imputer for completing missing values with simple strategies.

  - [Scholar.Metrics.Classification](Scholar.Metrics.Classification.md): Classification Metric functions.
  - [Scholar.Metrics.Clustering](Scholar.Metrics.Clustering.md): Metrics related to clustering algorithms.

  - [Scholar.Metrics.Distance](Scholar.Metrics.Distance.md): Distance metrics between multi-dimensional tensors.
They all support distance calculations between any subset of axes.

  - [Scholar.Metrics.Neighbors](Scholar.Metrics.Neighbors.md): Metrics for evaluating the results of approximate k-nearest neighbor search algorithms.

  - [Scholar.Metrics.Ranking](Scholar.Metrics.Ranking.md): Provides metrics and calculations related to ranking quality.
  - [Scholar.Metrics.Regression](Scholar.Metrics.Regression.md): Regression Metric functions.
  - [Scholar.Metrics.Similarity](Scholar.Metrics.Similarity.md): Similarity metrics between multi-dimensional tensors.

  - [Scholar.ModelSelection](Scholar.ModelSelection.md): Module containing cross validation, splitting function, and other model selection methods.

  - [Scholar.Preprocessing](Scholar.Preprocessing.md): Set of functions for preprocessing data.

  - [Scholar.Preprocessing.MaxAbsScaler](Scholar.Preprocessing.MaxAbsScaler.md): Scales a tensor by dividing each sample in batch by the maximum absolute value in the batch.
  - [Scholar.Preprocessing.MinMaxScaler](Scholar.Preprocessing.MinMaxScaler.md): Scales a tensor by dividing each sample in batch by maximum absolute value in the batch
  - [Scholar.Preprocessing.Normalizer](Scholar.Preprocessing.Normalizer.md): Implements functionality for rescaling tensor to unit norm. It enables to apply normalization along any combination of axes.

  - [Scholar.Preprocessing.OneHotEncoder](Scholar.Preprocessing.OneHotEncoder.md): Implements encoder that converts integer value (substitute of categorical data in tensors) into 0-1 vector.
The index of 1 in the vector is aranged in sorted manner. This means that for x < y => one_index(x) < one_index(y).
  - [Scholar.Preprocessing.OrdinalEncoder](Scholar.Preprocessing.OrdinalEncoder.md): Implements encoder that converts integer value (substitute of categorical data in tensors) into other integer value.
The values assigned starts from `0` and go up to `num_categories - 1`. They are maintained in sorted manner.
This means that for x < y => encoded_value(x) < encoded_value(y).
  - [Scholar.Preprocessing.StandardScaler](Scholar.Preprocessing.StandardScaler.md): Standardizes the tensor by removing the mean and scaling to unit variance.
  - [Scholar.Stats](Scholar.Stats.md): Statistical functions