Displaying 1 to 10 from 10 results

decisiontree - ID3-based implementation of the ML Decision Tree algorithm

  •    Ruby

A Ruby library which implements ID3 (information gain) algorithm for decision tree learning. Currently, continuous and discrete datasets can be learned.

GoJS - JavaScript diagramming library for interactive flowcharts, org charts, design tools, planning tools, visual languages

  •    Javascript

GoJS is a JavaScript and TypeScript library for creating and manipulating diagrams, charts, and graphs. GoJS is a flexible library that can be used to create a number of different kinds of interactive diagrams, including data visualizations, drawing tools, and graph editors. There are samples for flowchart, org chart, business process BPMN, swimlanes, timelines, state charts, kanban, network, mindmap, sankey, family trees and genogram charts, fishbone diagrams, floor plans, UML, decision trees, pert charts, Gantt, and hundreds more. GoJS includes a number of built in layouts including tree layout, force directed, radial, and layered digraph layout, and a number of custom layout examples.

scoruby - Ruby Scoring API for PMML

  •    Ruby

Ruby scoring API for Predictive Model Markup Language (PMML).Currently supports Decision Tree, Random Forest Naive Bayes and Gradient Boosted Models.

decision-tree-js - Small JavaScript implementation of ID3 Decision tree

  •    Javascript

Small JavaScript implementation of algorithm for training Decision Tree and Random Forest classifiers.

learningjs - javascript implementation of logistic regression/c4.5 decision tree

  •    Javascript

#Update I've made some update on the data loading logic so now it reads in csv-format file. Previous version is still accessible but it's no longer supported. #Introduction Javascript implementation of several machine learning algorithms including Decision Tree and Logistic Regression this far. More to come.

timbl - TiMBL implements several memory-based learning algorithms.

  •    C++

TiMBL is an open source software package implementing several memory-based learning algorithms, among which IB1-IG, an implementation of k-nearest neighbor classification with feature weighting suitable for symbolic feature spaces, and IGTree, a decision-tree approximation of IB1-IG. All implemented algorithms have in common that they store some representation of the training set explicitly in memory. During testing, new cases are classified by extrapolation from the most similar stored cases. For over fifteen years TiMBL has been mostly used in natural language processing as a machine learning classifier component, but its use extends to virtually any supervised machine learning domain. Due to its particular decision-tree-based implementation, TiMBL is in many cases far more efficient in classification than a standard k-nearest neighbor algorithm would be.

interpretable_machine_learning_with_python - Practical techniques for interpreting machine learning models

  •    Jupyter

Monotonicity constraints can turn opaque, complex models into transparent, and potentially regulator-approved models, by ensuring predictions only increase or only decrease for any change in a given input variable. In this notebook, I will demonstrate how to use monotonicity constraints in the popular open source gradient boosting package XGBoost to train a simple, accurate, nonlinear classifier on the UCI credit card default data. Once we have trained a monotonic XGBoost model, we will use partial dependence plots and individual conditional expectation (ICE) plots to investigate the internal mechanisms of the model and to verify its monotonic behavior. Partial dependence plots show us the way machine-learned response functions change based on the values of one or two input variables of interest, while averaging out the effects of all other input variables. ICE plots can be used to create more localized descriptions of model predictions, and ICE plots pair nicely with partial dependence plots. An example of generating regulator mandated reason codes from high fidelity Shapley explanations for any model prediction is also presented. The combination of monotonic XGBoost, partial dependence, ICE, and Shapley explanations is likely the most direct way to create an interpretable machine learning model today.

Artificial-Intelligence-and-Machine-Learning-Fundamentals - Get started with the development of real-world applications that are powered by the latest AI advances

  •    Python

Machine learning and neural networks are fast becoming pillars on which you can build intelligent applications. The course will begin by introducing you to Python and discussing using AI search algorithms. You will learn math-heavy topics, such as regression and classification, illustrated by Python examples.

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