Top 10 Jupyter Notebook random-forest Projects
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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.
I would use H20 if I were you. You can try out LLMs with a nice GUI. Unless you have some familiarity with the tools needed to run these projects, it can be frustrating. https://h2o.ai/
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I really like the simplicity of this framework, and they hit on a lot of common problems found in other agent-based frameworks. Most intrigued by the RAG improvements.
Seems like Microsoft was frustrated with the pace of movement in this space and the shitty results of agents (which admittedly kept my interest turned away from agents for the last few months). I'm interested again because it makes practical sense, and from looking at the example notebooks, seems fairly easy to integrate into existing applications.
Maybe this is the 'low code' approach that might actually work, and bridge together engineering and non-engineering resources.
This example was what caught my eye: https://github.com/microsoft/FLAML/blob/main/notebook/autoge...
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Project mention: Is there any algorithm that combines decision trees with regression models? | /r/learnmachinelearning | 2023-06-06
Sure is! Here’s an implementation
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Intrusion-Detection-System-Using-Machine-Learning
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
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STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA
Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks
Project mention: STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA: NEW Other Models - star count:100.0 | /r/algoprojects | 2023-04-29 -
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.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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Employees-Burnout-Analysis-and-Prediction
The "Employees Burnout Analysis and Prediction" GitHub repository is a project focused on analyzing and predicting employee burnout in an organization.
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CSGO-Pro-Gear-Performance-and-EDA
Modeling Professional (CS:GO) Gamer's Accuracy Performance Based on Gear and Settings, and Exploratory Data Analysis.
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Index
What are some of the best open-source random-forest projects in Jupyter Notebook? This list will help you:
Project | Stars | |
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1 | H2O | 6,684 |
2 | FLAML | 3,618 |
3 | dtreeviz | 2,804 |
4 | linear-tree | 321 |
5 | Intrusion-Detection-System-Using-Machine-Learning | 310 |
6 | STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA | 116 |
7 | NLU-engine-prototype-benchmarks | 5 |
8 | Employees-Burnout-Analysis-and-Prediction | 2 |
9 | CSGO-Pro-Gear-Performance-and-EDA | 1 |
10 | psych-verbs | 0 |