GitHub - ClimbsRocks/auto_ml: Automated machine learning for analytics & production. Untitled. Deploying a scikit-learn classifier to production. Scikit-learn is a great python library for all sorts of machine learning algorithms, and really well documented for the model development side of things.
But once you have a trained classifier and are ready to run it in production, how do you go about doing this? There’s a few managed services that will do it for you, but for my situation these weren’t a good fit. We just wanted to deploy the model onto a modest sized Digital Ocean instance, as a REST API that can be externally queried. Challenges saving the model in a form that can be loaded onto a remote serverwrapping up the classifier in an APIinstalling a scikit-learn environment on a serverfinally, deploying the code onto the remote server Model persistence The scikit docs have a good section on this topic: I’d recommend using joblib to serialize your model: I’m saving a single model under the key class1, but there’s scope to add several more classifiers as you go. Marcotcr/lime: Lime: Explaining the predictions of any machine learning classifier. 1602.04938. Converting to and from Document-Term Matrix and Corpus objects. R - LDA with topicmodels, how can I see which topics different documents belong to?
Machine Learning - Stanford University. Terryum/awesome-deep-learning-papers: The most cited deep learning papers. 1611.03530. Ismir02 rev. Spark MLlib for Decision Trees and Naive Bayes. In this tutorial, you will learn how to use Spark MLlib for the Decision Trees and Naive Bayes for classification or regression.
In the first part I will introduce the Decision Trees Algorithm and its uses in Spark MLlib. In the second part there will be some introduction of Naive Bayes. Part 1: Decision Tree Decision trees are widely used since they are easy to interpret, handle categorical features, extend to the multiclass classification setting, do not require feature scaling and are able to capture nonlinearities and feature interactions. Tree ensemble algorithms such as random forests and boosting are among the top performers for classification and regression tasks. MLlib supports decision trees for binary and multiclass classification and for regression, using both continuous and categorical features.
Introduction to Decision Trees. A decision tree is a model that uses a set of criteria to classify something.
Suppose you tell your single friend Bill to go out with your new friend Sally. Since Bill has never met Sally, he asks you a series of questions. Implementation of k-means Clustering - Edureka. In this blog, you will understand what is K-means clustering and how it can be implemented on the criminal data collected in various US states.
The data contains crimes committed like: assault, murder, and rape in arrests per 100,000 residents in each of the 50 US states in 1973. Along with analyzing the data you will also learn about: Finding the optimal number of clusters.Minimizing distortionCreating and analyzing the elbow curve.Understanding the mechanism of k-means algorithm. Let us start with the analysis. The data looks as: Click on the image to download this dataset Need this dataset? First let’s prepare the data for the analysis. > crime0 <- na.omit(USArrests) > crime <- data.matrix (crime0) > str(crime) num [1:50, 1:4] 13.2 10 8.1 8.8 9 7.9 3.3 5.9 15.4 17.4 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:50] "Alabama" "Alaska" "Arizona" "Arkansas" ... ..$ : chr [1:4] "Murder" "Assault" "UrbanPop" "Rape" Let us take the number of clusters to be 5.
Une introduction aux arbres de décision. Les arbres de décision sont l’une des structures de données majeures de l’apprentissage statistique.
Leur fonctionnement repose sur des heuristiques qui, tout en satisfaisant l’intuition, donnent des résultats remarquables en pratique (notamment lorsqu’ils sont utilisés en « forêts aléatoires »). Leur structure arborescente les rend également lisibles par un être humain, contrairement à d’autres approches où le prédicteur construit est une « boîte noire ». L’introduction que nous proposons ici décrit les bases de leur fonctionnement tout en apportant quelques justifications théoriques. Nous aborderons aussi (brièvement) l’extension aux Random Forests. On supposera le lecteur familier avec le contexte général de l’apprentissage supervisé.1 Suivez le lien pour la version PDF. Table des matières Un arbre de décision modélise une hiérarchie de tests sur les valeurs d’un ensemble de variables appelées attributs. Best Machine Learning Resources for Getting Started. This was a really hard post to write because I want it to be really valuable.
I sat down with a blank page and asked the really hard question of what are the very best libraries, courses, papers and books I would recommend to an absolute beginner in the field of Machine Learning. I really agonised over what to include and what to exclude. I had to work hard to put my self in the shoes of a programmer and beginner at machine learning and think about what resources would best benefit them. I picked the best for each type of resource. Neural Networks for Machine Learning - University of Toronto. About the Course Neural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like.
As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods. They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images. The course will explain the new learning procedures that are responsible for these advances, including effective new proceduresr for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains. Recommended Background Programming proficiency in Matlab, Octave or Python. Course Format.