fdm2id

Classification using AdaBoost

Classification using APRIORI

Classification using Bagging

Correspondence Analysis (CA)

Classification using CART

Classification using Canonical Discriminant Analysis

DBSCAN clustering method

Expectation-Maximization clustering method

Classification with Feature selection

Classification using Gradient Boosting

Hierarchical Cluster Analysis method

Kernel Regression

K-means method

Classification using k-NN

Classification using Linear Discriminant Analysis

Linear Regression

Classification using Logistic Regression

Multiple Correspondence Analysis (MCA)

MeanShift method

Classification using Multilayer Perceptron

Multi-Layer Perceptron Regression

Classification using Naive Bayes

Non-negative Matrix Factorization

Principal Component Analysis (PCA)

Polynomial Regression

Classification using Quadratic Discriminant Analysis

Classification using Random Forest

Self-Organizing Maps clustering method

Spectral clustering method

Classification using one-level decision tree

Singular Value Decomposition

Classification using Support Vector Machine

Classification using Support Vector Machine with a linear kernel

Classification using Support Vector Machine with a radial kernel

Regression using Support Vector Machine

Regression using Support Vector Machine with a linear kernel

Regression using Support Vector Machine with a radial kernel

Text mining

t-distributed Stochastic Neighbor Embedding

Sample of car accident location in the UK during year 2014.

Alcohol dataset

APRIORI classification model

Auto MPG dataset

Flea beetles dataset

Birth dataset

Boosting methods model

Clustering Box Plots

Population and location of 18 major british cities.

Depth

CART information

Number of Leafs

Number of Nodes

CART Plot

Canonical Disciminant Analysis model

Close a graphics device

Comparison of two sets of clusters

Comparison of two sets of clusters, using accuracy

Comparison of two sets of clusters, using Jaccard index

Comparison of two sets of clusters, using kappa

Confuion matrix

Cookies dataset

Plot the Cook's distance of a linear regression model

Correlated variables

Plot Cost Curves

Credit dataset

Square dataset

Gaussian mixture dataset

Parabol dataset

Target1 dataset

Target2 dataset

Two moons dataset

XOR dataset

"data1" dataset

"data2" dataset

"data3" dataset

Training set and test set

DBSCAN model

Decathlon dataset

Plot a k-distance graphic

Expectation-Maximization model

Eucalyptus dataset

Evaluation of classification or regression predictions

Accuracy of classification predictions

F-measure

Fowlkes–Mallows index

Goodness

Jaccard index

Kappa evaluation of classification predictions

MSEP evaluation of regression predictions

Precision of classification predictions

R2 evaluation of regression predictions

Recall of classification predictions

Open a graphics device

Factorial analysis results

Filtering a set of rules

Frequent words

Remove redundancy in a set of rules

Extract words and phrases from a corpus

Clustering evaluation through internal criteria

Clustering evaluation through Dunn's index

Clustering evaluation through interclass inertia

Clustering evaluation through intraclass inertia

Ionosphere dataset

Keiser rule

Estimation of the number of clusters for K-means

K Nearest Neighbours model

Plot the leverage points of a linear regression model

Linsep dataset

load a text file

MeanShift model

Generic classification or regression model

Movies dataset

Ozone dataset

Learning Parameters

Performance estimation

Plot function for cda-class

Plot function for factorial-class

Plot function for som-class

Plot word cloud

Generic Plot Method for Clustering

Advanced plot function

Plot rank versus frequency

Model predictions

Model predictions

Model predictions

Predict function for DBSCAN

Predict function for EM

Predict function for K-means

Model predictions

Predict function for MeanShift

Model predictions

Model predictions

Model predictions

Print a classification model obtained by APRIORI

Plot function for factorial-class

Pseudo-F

Document query

Word query

reg1 dataset

reg2 dataset

Plot function for a regression model

Plot the studentized residuals of a linear regression model

Plot ROC Curves

Rotation

Running time

Clustering Scatter Plots

Feature selection for classification

Feature selection

Snore dataset

Self-Organizing Maps model

Spectral clustering model

Spine dataset

Splits a dataset into training set and test set

Clustering evaluation through stability

Print summary of a classification model obtained by APRIORI

Temperature dataset

Text mining object

Titanic dataset

Toggle graphic exports

Dendrogram Plots

University dataset

Document vectorization

Word vectorization

Document vectorization object

Vowels dataset

Wheat dataset

Wine dataset

Zoo dataset