4.2. Algorithmes

Comparaison des méthodes de classification

A comparison of a several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be taken with a grain of salt, as the intuition conveyed by these examples does not necessarily carry over to real datasets. Particularly in high-dimensional spaces,… Lire la suite Comparaison des méthodes de classification

4.2. Algorithmes

Comparaison des méthodes de clustering

http://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html This example aims at showing characteristics of different clustering algorithms on datasets that are “interesting” but still in 2D. The last dataset is an example of a ‘null’ situation for clustering: the data is homogeneous, and there is no good clustering. While these examples give some intuition about the algorithms, this intuition might not… Lire la suite Comparaison des méthodes de clustering