4.2. Algorithmes

Exemples d’application de la classification naïve bayésienne

Pour comprendre comment fonctionne la classification naïve bayésienne : https://actuairesbigdata.wordpress.com/2016/01/13/une-explication-simple-de-classification-naive-bayesienne/ Pour voir quelques exemples d’application, le package R e1071 propose la fonction naiveBayes : ## Categorical data only: data(HouseVotes84, package = « mlbench ») model <- naiveBayes(Class ~ ., data = HouseVotes84) predict(model, HouseVotes84[1:10,]) predict(model, HouseVotes84[1:10,], type = « raw »)   pred <- predict(model, HouseVotes84) table(pred, HouseVotes84$Class)  … Lire la suite Exemples d’application de la classification naïve bayésienne

4.2. Algorithmes

Une explication simple de classification naïve bayésienne

http://stackoverflow.com/questions/10059594/a-simple-explanation-of-naive-bayes-classification First, Conditional Probability & Bayes’ Rule Before someone can understand and appreciate the nuances of Naive Bayes’, they need to know a couple of related concepts first, namely, the idea of Conditional Probability, and Bayes’ Rule. (If you are familiar with these concepts, skip to the section titled Getting to Naive Bayes’) Conditional Probability… Lire la suite Une explication simple de classification naïve bayésienne

4.2. Algorithmes

Niveaux de Super Mario et réseaux de neurones

Machine Learning is Fun! Part 2 Using Machine Learning to generate Super Mario Maker levels In Part 1, we said that Machine Learning is using generic algorithms to tell you something interesting about your data without writing any code specific to the problem you are solving. (If you haven’t already read part 1, read it… Lire la suite Niveaux de Super Mario et réseaux de neurones

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