Used by the CART (classification and regression tree) algorithm, Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it were randomly labeled according to the distribution of labels in the subset. Gini impurity can be computed by summing the probability of each item being chosen times the probability of a mistake in categorizing that item. It reaches its minimum (zero) when all cases in the node fall into a single target category. … Gini Impurity

## Main functions in the factoextra package

See the online documentation (http://www.sthda.com/english/rpkgs/factoextra) for a complete list.

### Visualizing dimension reduction analysis outputs

Functions | Description |
---|---|

fviz_eig (or fviz_eigenvalue) |
Extract and visualize the eigenvalues/variances of dimensions. |

fviz_pca |
Graph of individuals/variables from the output of Principal Component Analysis (PCA). |

fviz_ca |
Graph of column/row variables from the output of Correspondence Analysis (CA). |

fviz_mca |
Graph of individuals/variables from the output of Multiple Correspondence Analysis (MCA). |

fviz_mfa |
Graph of individuals/variables from the output of Multiple Factor Analysis (MFA). |

fviz_famd |
Graph of individuals/variables from the output of Factor Analysis of Mixed Data (FAMD). |

fviz_hmfa |
Graph of individuals/variables from the output of Hierarchical Multiple Factor Analysis (HMFA). |

fviz_ellipses |
Draw confidence ellipses around the categories. |

fviz_cos2 |
Visualize the quality of representation of the row/column variable from the results of PCA, CA, MCA functions. |

fviz_contrib |
Visualize the contributions of row/column elements from the results of PCA, CA, MCA functions. |

### Extracting data from dimension reduction analysis outputs

Functions | Description |
---|---|

get_eigenvalue |
Extract and visualize the eigenvalues/variances of dimensions. |

get_pca |
Extract all the results (coordinates, squared cosine, contributions) for the active individuals/variables from Principal Component Analysis (PCA) outputs. |

get_ca |
Extract all the results (coordinates, squared cosine, contributions) for the active column/row variables from Correspondence Analysis outputs. |

get_mca |
Extract results from Multiple Correspondence Analysis outputs. |

get_mfa |
Extract results from Multiple Factor Analysis outputs. |

get_famd |
Extract results from Factor Analysis of Mixed Data outputs. |

get_hmfa |
Extract results from Hierarchical Multiple Factor Analysis outputs. |

facto_summarize |
Subset and summarize the output of factor analyses. |

### Clustering analysis and visualization

Functions | Description |
---|---|

dist(fviz_dist, get_dist) |
Enhanced Distance Matrix Computation and Visualization. |

get_clust_tendency |
Assessing Clustering Tendency. |

fviz_nbclust(fviz_gap_stat) |
Determining and Visualizing the Optimal Number of Clusters. |

fviz_dend |
Enhanced Visualization of Dendrogram |

fviz_cluster |
Visualize Clustering Results |

fviz_mclust |
Visualize Model-based Clustering Results |

fviz_silhouette |
Visualize Silhouette Information from Clustering. |

hcut |
Computes Hierarchical Clustering and Cut the Tree |

hkmeans (hkmeans_tree, print.hkmeans) |
Hierarchical k-means clustering. |

eclust |
Visual enhancement of clustering analysis |