Random Forest Classification for Neural Subtypes

Overview

Random Forest Classification for Neural Subtypes

This notebook is focussed on stepping through a Random Forest classifier for neural subtypes extracted from extracellular recordings from human brain organoids. We will step through multi-demsionality reduction, clustering, waveform plotting and confusion matrices. Notably, this is a simple data analysis notebook for the purpose of a Medium & Kaggle contribution.

Waveform plotting

Multi-dimensionality reduction

Clustering

Data breakdown

Random forest

Owner
Michael Zabolocki
PhD candidate in Human Neurophysiology & Genetics.
Michael Zabolocki
Distributed Computing for AI Made Simple

Project Home Blog Documents Paper Media Coverage Join Fiber users email list Uber Open Source 997 Dec 30, 2022

This project has Classification and Clustering done Via kNN and K-Means respectfully

This project has Classification and Clustering done Via kNN and K-Means respectfully. It later tests its efficiency via F1/accuracy/recall/precision for kNN and Davies-Bouldin Index for Clustering. T

Mohammad Ali Mustafa 0 Jan 20, 2022
A Streamlit demo to interactively visualize Uber pickups in New York City

Streamlit Demo: Uber Pickups in New York City A Streamlit demo written in pure Python to interactively visualize Uber pickups in New York City. View t

Streamlit 230 Dec 28, 2022
Scikit learn library models to account for data and concept drift.

liquid_scikit_learn Scikit learn library models to account for data and concept drift. This python library focuses on solving data drift and concept d

7 Nov 18, 2021
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

Chao Ma 3k Jan 08, 2023
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. 10x Larger Models 10x Faster Trainin

Microsoft 8.4k Dec 30, 2022
InfiniteBoost: building infinite ensembles with gradient descent

InfiniteBoost Code for a paper InfiniteBoost: building infinite ensembles with gradient descent (arXiv:1706.01109). A. Rogozhnikov, T. Likhomanenko De

Alex Rogozhnikov 183 Jan 03, 2023
Automated machine learning: Review of the state-of-the-art and opportunities for healthcare

Automated machine learning: Review of the state-of-the-art and opportunities for healthcare

42 Dec 23, 2022
Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and t

164 Jan 04, 2023
Predicting Keystrokes using an Audio Side-Channel Attack and Machine Learning

Predicting Keystrokes using an Audio Side-Channel Attack and Machine Learning My

3 Apr 10, 2022
scikit-learn is a python module for machine learning built on top of numpy / scipy

About scikit-learn is a python module for machine learning built on top of numpy / scipy. The purpose of the scikit-learn-tutorial subproject is to le

Gael Varoquaux 122 Dec 12, 2022
Quantum Machine Learning

The Machine Learning package simply contains sample datasets at present. It has some classification algorithms such as QSVM and VQC (Variational Quantum Classifier), where this data can be used for e

Qiskit 364 Jan 08, 2023
🤖 ⚡ scikit-learn tips

🤖 ⚡ scikit-learn tips New tips are posted on LinkedIn, Twitter, and Facebook. 👉 Sign up to receive 2 video tips by email every week! 👈 List of all

Kevin Markham 1.6k Jan 03, 2023
A toolbox to iNNvestigate neural networks' predictions!

iNNvestigate neural networks! Table of contents Introduction Installation Usage and Examples More documentation Contributing Releases Introduction In

Maximilian Alber 1.1k Jan 05, 2023
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Machine Learning Notebooks, 3rd edition This project aims at teaching you the fundamentals of Machine Learning in python. It contains the example code

Aurélien Geron 1.6k Jan 05, 2023
MegFlow - Efficient ML solutions for long-tailed demands.

Efficient ML solutions for long-tailed demands.

旷视天元 MegEngine 371 Dec 21, 2022
Software Engineer Salary Prediction

Based on 2021 stack overflow data, this machine learning web application helps one predict the salary based on years of experience, level of education and the country they work in.

Jhanvi Mimani 1 Jan 08, 2022
Open MLOps - A Production-focused Open-Source Machine Learning Framework

Open MLOps - A Production-focused Open-Source Machine Learning Framework Open MLOps is a set of open-source tools carefully chosen to ease user experi

Data Revenue 590 Dec 28, 2022
Evaluate on three different ML model for feature selection using Breast cancer data.

Anomaly-detection-Feature-Selection Evaluate on three different ML model for feature selection using Breast cancer data. ML models: SVM, KNN and MLP.

Tarek idrees 1 Mar 17, 2022
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

Little Ball of Fur is a graph sampling extension library for Python. Please look at the Documentation, relevant Paper, Promo video and External Resour

Benedek Rozemberczki 619 Dec 14, 2022