All Classes and Interfaces
Class
Description
Abstract base class of classifiers.
The accuracy is the proportion of true results (both true positives and
true negatives) in the population.
The activation function in hidden layers.
The activation function.
AdaBoost (Adaptive Boosting) classifier with decision trees.
Adaptive Moment optimizer.
Adjusted Mutual Information (AMI) for comparing clustering.
The normalization method.
Adjusted Rand Index.
Autoregressive model.
The fitting method.
Association Rule Mining.
Autoregressive moving-average model.
Association rule object.
The area under the curve (AUC).
A bag of random selected samples.
The bag-of-words feature of text used in natural language
processing and information retrieval.
Balanced Box-Decomposition Tree.
The response variable is of Bernoulli distribution.
Encodes categorical features using sparse one-hot scheme.
The response variable is of Binomial distribution.
Balanced Iterative Reducing and Clustering using Hierarchies.
The bootstrap is a general tool for assessing statistical accuracy.
Portmanteau test jointly that several autocorrelations of time series
are zero.
The type of test.
Classification and regression tree.
In centroid-based clustering, clusters are represented by a central vector,
which may not necessarily be a member of the data set.
Clustering Large Applications based upon RANdomized Search.
An abstract interface to measure the classification performance.
The classification validation metrics.
Classification model validation results.
Classification model validation results.
A classifier assigns an input object into one of a given number of categories.
The classifier trainer.
Map arbitrary class labels to [0, k), where k is the number of classes.
An abstract interface to measure the clustering performance.
Complete linkage.
The confusion matrix of truth and predictions.
The contingency table.
Neural network cost function.
First-order linear conditional random field.
First-order CRF sequence labeler.
Cross entropy generalizes the log loss metric to multiclass problems.
Cross-validation is a technique for assessing how the results of a
statistical analysis will generalize to an independent data set.
Classification trait on DataFrame.
The classifier trainer.
Regression trait on DataFrame.
The regression trainer.
Density-Based Spatial Clustering of Applications with Noise.
A leaf node in decision tree.
Decision tree.
DENsity CLUstering.
Deterministic annealing clustering.
Naive Bayes classifier for document classification in NLP.
The generation models of naive Bayes classifier.
The connection between neurons.
Elastic Net regularization.
The number of errors in the population.
Fall-out, false alarm rate, or false positive rate (FPR)
The false discovery rate (FDR) is ratio of false positives
to combined true and false positives, which is actually 1 - precision.
Fisher's linear discriminant.
Frequent item set mining based on the FP-growth (frequent pattern growth)
algorithm, which employs an extended prefix-tree (FP-tree) structure to
store the database in a compressed form.
FP-tree data structure used in FP-growth (frequent pattern growth)
algorithm for frequent item set mining.
The F-score (or F-measure) considers both the precision and the recall of the test
to compute the score.
Genetic algorithm based feature selection.
Gaussian Process for Regression.
Generalized Hebbian Algorithm.
Generalized linear models.
G-Means clustering algorithm, an extended K-Means which tries to
automatically determine the number of clusters by normality test.
Gradient boosting for classification.
Gradient boosting for regression.
Growing Neural Gas.
Feature hashing, also known as the hashing trick, is a fast and
space-efficient way of vectorizing features, i.e.
A hidden layer in the neural network.
The builder of hidden layers.
Agglomerative Hierarchical Clustering.
First-order Hidden Markov Model.
First-order Hidden Markov Model sequence labeler.
Hyperparameter configuration.
Information Value (IV) measures the predictive strength of a feature
for a binary dependent variable.
An input layer in the neural network.
An internal node in CART.
Isolation forest is an unsupervised learning algorithm for anomaly
detection that works on the principle of isolating anomalies.
Isolation tree.
Isometric feature mapping.
Kruskal's non-metric MDS.
A method to calibrate decision function value to probability.
A set of items.
Kernel machines.
The learning methods building on kernels.
Kernel PCA transform.
K-Means clustering.
Missing value imputation by K-Medoids clustering.
K-Modes clustering.
K-nearest neighbor classifier.
Missing value imputation with k-nearest neighbors.
Kernel principal component analysis.
Laplacian Eigenmap.
Lasso (least absolute shrinkage and selection operator) regression.
LASVM is an approximate SVM solver that uses online approximation.
A layer in the neural network.
The builder of layers.
Linear discriminant analysis.
A leaf node in decision tree.
The leaky rectifier activation function
max(x, ax) where
0 <= a < 1.Linear kernel machine.
Linear model.
A measure of dissimilarity between clusters (i.e.
Locally Linear Embedding.
Logistic regression.
Binomial logistic regression.
Multinomial logistic regression.
Log loss is a evaluation metric for binary classifiers and it is sometimes
the optimization objective as well in case of logistic regression and neural
networks.
Leave-one-out cross validation.
Regression loss function.
The type of loss.
Mean absolute deviation error.
Matthews correlation coefficient.
Scales each feature by its maximum absolute value.
Maximum Entropy Classifier.
Binomial maximum entropy classifier.
Multinomial maximum entropy classifier.
Classical multidimensional scaling, also known as principal coordinates
analysis.
Non-parametric Minimum Conditional Entropy Clustering.
Fully connected multilayer perceptron neural network for classification.
Fully connected multilayer perceptron neural network for regression.
The GLM model specification.
Model selection criteria.
Mean squared error.
Fully connected multilayer perceptron neural network.
Mutual Information for comparing clustering.
Naive Bayes classifier.
The neighborhood function for 2-dimensional lattice topology (e.g.
Neural Gas soft competitive learning algorithm.
NeuralMap is an efficient competitive learning algorithm inspired by growing
neural gas and BIRCH.
The neuron vertex in the growing neural gas network.
CART tree node.
A node with a nominal split variable.
The data about of a potential split for a leaf node.
Normalized Mutual Information (NMI) for comparing clustering.
The normalization method.
Normalize samples individually to unit norm.
Vector norm.
One-class support vector machine.
Ordinary least squares.
One-vs-one strategy for reducing the problem of
multiclass classification to multiple binary classification problems.
One-vs-rest (or one-vs-all) strategy for reducing the problem of
multiclass classification to multiple binary classification problems.
The neural network optimizer.
A node with a ordinal split variable (real-valued or ordinal categorical value).
The data about of a potential split for a leaf node.
The output function of neural networks.
The output layer in the neural network.
The builder of output layers.
Partition clustering.
Principal component analysis.
Platt scaling or Platt calibration is a way of transforming the outputs
of a classification model into a probability distribution over classes.
The response variable is of Poisson distribution.
The precision or positive predictive value (PPV) is ratio of true positives
to combined true and false positives, which is different from sensitivity.
An abstract interface to measure the probabilistic classification performance.
Probabilistic principal component analysis.
A projection is a kind of feature extraction technique that transforms data
from the input space to a feature space, linearly or non-linearly.
Quadratic discriminant analysis.
R2.
Rand Index.
Random forest for classification.
Random forest for regression.
The base model.
The base model.
Random projection is a promising dimensionality reduction technique for
learning mixtures of Gaussians.
A neuron in radial basis function network.
Radial basis function networks.
Radial basis function network.
Regularized discriminant analysis.
In information retrieval area, sensitivity is called recall.
Regression analysis includes any techniques for modeling and analyzing
the relationship between a dependent variable and one or more independent
variables.
The regression trainer.
An abstract interface to measure the regression performance.
The regression validation metrics.
A leaf node in regression tree.
Regression tree.
Regression model validation results.
Regression model validation results.
The rectifier activation function
max(0, x).Ridge Regression.
Root mean squared error.
RMSProp optimizer with adaptive learning rate.
Robustly standardizes numeric feature by subtracting
the median and dividing by the IQR.
Residual sum of squares.
The Sammon's mapping is an iterative technique for making interpoint
distances in the low-dimensional projection as close as possible to the
interpoint distances in the high-dimensional object.
Scales the numeric variables into the range [0, 1].
Sensitivity or true positive rate (TPR) (also called hit rate, recall) is a
statistical measures of the performance of a binary classification test.
A sequence labeler assigns a class label to each position of the sequence.
Stochastic gradient descent (with momentum) optimizer.
SHAP (SHapley Additive exPlanations) is a game theoretic approach to
explain the output of any machine learning model.
The Sequential Information Bottleneck algorithm.
Logistic sigmoid function: sigmoid(v)=1/(1+exp(-v)).
The signal-to-noise (S2N) metric ratio is a univariate feature ranking metric,
which can be used as a feature selection criterion for binary classification
problems.
Simple algorithm replaces missing values with the constant value
along each column.
Single linkage.
Softmax for multi-class cross entropy objection function.
Self-Organizing Map.
Encodes numeric and categorical features into sparse array
with on-hot encoding of categorical variables.
Logistic regression on sparse data.
Binomial logistic regression.
Multinomial logistic regression.
Specificity (SPC) or True Negative Rate is a statistical measures of the
performance of a binary classification test.
Spectral Clustering.
The data about of a potential split for a leaf node.
The criterion to choose variable to split instances.
Standardizes numeric feature to 0 mean and unit variance.
The ratio of between-groups to within-groups sum of squares is a univariate
feature ranking metric, which can be used as a feature selection criterion
for multi-class classification problems.
Support vector.
Missing value imputation with singular value decomposition.
One-class support vector machines for novelty detection.
Support vector machines for classification.
Epsilon support vector regression.
Epsilon support vector regression.
Hyperbolic tangent activation function.
Time series utility functions.
SHAP of ensemble tree methods.
The t-distributed stochastic neighbor embedding.
Uniform Manifold Approximation and Projection.
Unweighted Pair Group Method with Arithmetic mean (also known as average linkage).
Unweighted Pair Group Method using Centroids (also known as centroid linkage).
Vector quantizer with competitive learning.
Ward's linkage.
Scales all numeric variables into the range [0, 1].
Weighted Pair Group Method with Arithmetic mean.
Weighted Pair Group Method using Centroids (also known as median linkage).
X-Means clustering algorithm, an extended K-Means which tries to
automatically determine the number of clusters based on BIC scores.