fdm2id0.9.9 package

Classification using one-level decision tree

Print summary of a classification model obtained by APRIORI

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 object

Text mining

Toggle graphic exports

Dendrogram Plots

t-distributed Stochastic Neighbor Embedding

Classification using AdaBoost

APRIORI classification model

Classification using APRIORI

Duplicate and add noise to a dataset

Classification using Bagging

Boosting methods model

Clustering Box Plots

Correspondence Analysis (CA)

Classification using CART

Depth

CART information

Number of Leafs

Number of Nodes

CART Plot

Canonical Disciminant Analysis model

Classification using Canonical Discriminant Analysis

Close a graphics device

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

Comparison of two sets of clusters

Confuion matrix

Plot the Cook's distance of a linear regression model

Correlated variables

Plot Cost Curves

Square dataset

Gaussian mixture dataset

Parabol dataset

Target1 dataset

Target2 dataset

Two moons dataset

XOR dataset

Training set and test set

DBSCAN model

DBSCAN clustering method

Plot a k-distance graphic

Expectation-Maximization model

Expectation-Maximization clustering method

Accuracy of classification predictions

Adjusted R2 evaluation of regression 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

Evaluation of classification or regression predictions

Recall of classification predictions

Open a graphics device

Factorial analysis results

Classification with Feature selection

Filtering a set of rules

Frequent words

Remove redundancy in a set of rules

Extract words and phrases from a corpus

Classification using Gradient Boosting

Hierarchical Cluster Analysis method

Clustering evaluation through Dunn's index

Clustering evaluation through interclass inertia

Clustering evaluation through intraclass inertia

Clustering evaluation through internal criteria

Kaiser rule

Kernel Regression

Estimation of the number of clusters for K-means

K-means method

K Nearest Neighbours model

Classification using k-NN

Classification using Linear Discriminant Analysis

Plot the leverage points of a linear regression model

Linear Regression

load a text file

Classification using Logistic Regression

Multiple Correspondence Analysis (MCA)

MeanShift model

MeanShift method

Classification using Multilayer Perceptron

Multi-Layer Perceptron Regression

Generic classification or regression model

Classification using Naive Bayes

Non-negative Matrix Factorization

Learning Parameters

Principal Component Analysis (PCA)

Performance estimation

Plot function for cda-class

Plot function for factorial-class

Plot function for som-class

Plot actual vs. predictions

Plot word cloud

Generic Plot Method for Clustering

Advanced plot function

Plot rank versus frequency

Polynomial Regression

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

Classification using Quadratic Discriminant Analysis

Document query

Word query

Classification using Random Forest

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

Self-Organizing Maps model

Self-Organizing Maps clustering method

Spectral clustering model

Spectral clustering method

Splits a dataset into training set and test set

Clustering evaluation through stability

Document vectorization

Word vectorization

Document vectorization object