dtreeviz
Employees-Burnout-Analysis-and-Prediction
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| dtreeviz | Employees-Burnout-Analysis-and-Prediction | |
|---|---|---|
| 3 | 1 | |
| 2,836 | 2 | |
| - | - | |
| 5.4 | 7.3 | |
| 4 months ago | 5 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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dtreeviz
- Dtreeviz: Decision Tree Visualization
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Pybaobab – Python implementation of visualization technique for decision trees
Not really. If we are doing data science for many companies and explainability is an aspect we will likely go for decision trees if the lift for advanced models is minor anyways. We use this (not quite as pretty for visualization but extremely useful to get a grasp if the tree model): https://github.com/parrt/dtreeviz
- How to Visualize Decision Trees
Employees-Burnout-Analysis-and-Prediction
What are some alternatives?
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
awesome-data-centric-ai - Open-Source Software, Tutorials, and Research on Data-Centric AI 🤖
eli5 - A library for debugging/inspecting machine learning classifiers and explaining their predictions
machine_learning_complete - A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.
linear-tree - A python library to build Model Trees with Linear Models at the leaves.
NLU-engine-prototype-benchmarks - Demo and benchmarks for building an NLU engine similar to those in voice assistants. Several intent classifiers are implemented and benchmarked. Conditional Random Fields (CRFs) are used for entity extraction.
feature-engineering-tutorials - Data Science Feature Engineering and Selection Tutorials
VevestaX - 2 Lines of code to track ML experiments + EDA + check into Github
psych-verbs - Research experiment design and classification of Romanian emotion verbs
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.