Reverse Dependencies of seaborn
The following projects have a declared dependency on seaborn:
- mitoscripts — To assist in quantifying mitochondrial morphology
- mitten — A package for multivariate statistical process control modeling.
- MiV-OS — Python software for analysis and computing framework used in MiV project.
- mix-mavis — MAVIS 数据分析工具
- mixician — An advanced hybrid ranking engine for recommendation systems, designed to automate the optimization of algorithms and parameters tailored to diverse business objectives.
- mkdocs-matplotlib — Live rendering of python code using matplotlib
- mkeima — Analysis of flow cytometry-based mKeima assays in Python
- mkreports — A package to programmatically create mkdocs sites.
- mkt-retv — market research TV
- ML-Classification-model-selector-Basavaraj100 — It select best classfication model
- ML-Education-Tools — ML Education Tools for Teaching
- ml-env — no summary
- ml-express — A Python library for day to day data analysis and machine learning.
- ml-helper — Helpers to speed up and structure machine learning projects
- ml-init — Install the main ML libraries
- ML-medic-kit — The Machine Learning Medic Kit is designed to enhance the capabilities of health data scientists tackling binary classification problems
- ml-mosaic — Mosaic is a framework dedicated to the comparison of AI models.
- ML-Navigator — ML-Navigator is a tutorial-based Machine Learning framework. The main component of ML-Navigator is the flow. A flow is a collection of compact methods/functions that can be stuck together with guidance texts.
- ml-processor — Includes functions for performing econometrics tasks
- ml-solution — A mechine learning pipeline lib.
- ml-study — python machine learning structure architecture
- ml-swissknife — Reusable ML research primitives for fast prototyping.
- ml4chem — Machine learning for chemistry and materials.
- ml4floods — Machine learning models for end-to-end flood extent segmentation.
- ml4pd — ML4PD - an open-source libray for building Aspen-like process models via machine learning.
- ml4qc — Toolkit for ML-based survey quality control
- mlaction — Statistical Learning Method package
- MLAutoEDA — Helper functions to automate Exploratory Data Analysis. The focus is mainly on typical Machine Learning datasets, where there is a target variable (numerical or categorical) and the aim of EDA is to get a sense of the data and whether the variables may be useful for the prediction or not.
- mlb-fantasy — MLB fantasy draft optimizer. Requires a draft_picks.txt file with the format: "Player Name: Team 1, Team 2, Team 3, Team 4, Team 5, Team 6" with each player picks on a new line.
- mlbase — no summary
- mlcomposer — Treinamento e avaliação de modelos de machine learning através de funções e classes encapsuladas
- mlconfound — Tools for analyzing and quantifying effects of counfounder variables on machine learning model predictions.
- mle-hyperopt — Machine Learning Experiment Hyperparameter Optimization
- mle-logging — Machine Learning Experiment Logging
- mle-toolbox — Machine Learning Experiment Toolbox
- mlearner — Machine Learning Library Extensions
- MLexamples — Machine Learning
- mlexhibit — A high-level light-weight library for exhibiting data and machine learning analytics.
- mlexpy — Simple utilities for handling and managing exploratory and experimental machine learning development.
- mlflow-extend — Extend MLflow's functionality
- mlfompy — MLFoMPy is an effective tool that extracts the main figures of merit (FoM) of a semiconductors IV curve
- mlframe — mlframe package.
- mlgauge — Library to compare machine learning methods across datasets
- mlhybridx — ML module
- mlinspect — Inspect ML Pipelines in the form of a DAG
- mlk — ML Kit
- mllibs — Simplifying Machine Learning
- MLLytics — A library of tools for easier evaluation of ML models.
- mlmachine — Accelerate machine learning experimentation
- mlnext-framework — Machine learning utilities.
- MLOne — A Python package with which users can just drop their dataset and download the best ML model for their dataset
- mlops-build — MLops course
- mlops-ods — no summary
- mlops_training_pipeline — no summary
- mlopsrobotics — no summary
- mloptimizer — mloptimizer is a Python library for optimizing hyperparameters of machine learning algorithms using genetic algorithms.
- mlr — Linear regression utility with inference tests, residual analysis, outlier visualization, multicollinearity test, and other features
- mlrap — Machine Learning Regression Analyse Packages
- mlviz — mlviz is a visualization and graphics helpers for common machine learning work
- MLVRAtests — Set of tools to measure and test for violations of the 1982 Voting Rights Amendment
- mlwhatif — Data-Centric What-If Analysis for Native Machine Learning Pipelines
- MLXpress — A powerful and user-friendly machine learning toolkit for data science and ML professionals to accelerate their workflow
- mmdatascienceutils — A utils package for data science projects
- mmdet — OpenMMLab Detection Toolbox and Benchmark
- mmtfPyspark — Methods for parallel and distributed analysis and mining of the Protein Data Bank using MMTF and Apache Spark
- mmtrack — OpenMMLab Unified Video Perception Platform
- mne-bids-pipeline — A full-flegded processing pipeline for your MEG and EEG data
- mne-nirs — An MNE compatible package for processing near-infrared spectroscopy data.
- mne-vision — MNE Vision is a GUI for MNE
- mngs — For lazy python users (monogusa people in Japanse), especially in ML/DSP fields
- moabb — Mother of All BCI Benchmarks
- MOBiceps — Python tools for Mass Spectrometry and Omics data.
- mobilkit — A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data
- moca — Tool for motif conservation analysis
- moclaphar — This packages mainly aims to make an easy process for dataset manipulation.
- MoClust — A deep learning-based clustering method for single-cell multi-omics data
- modbp — This is a community detection package that used a belief propagation approach to optimize modularity on multilayer networks. Algorithm is implemented in c++ with python interface for convenience.
- model-fkeywords — A Natural Language Processing Library
- model-inspector — Inspect machine learning models
- model21cm — Infer parameters of a 21cm cosmology model.
- ModelAuto — Speed up your model making process. This will help you in selecting best features, best Models (SVM,SVR Random Fotest e.t.c and also in Data Preprocessing
- modelcomp — Compare between performances of machine learning models with ease.
- ModelFlowIb — A tool to solve and manage dynamic economic models
- modelfree-protein15n — Model free analysis of protein backbone amide 15N spin relaxation rates.
- modelling-utils — Utility functions and data structure for aiding in Analog Integrated Circuit Modelling using Python
- modelvis — Visualising Machine Learning Models
- modularitypruning — Pruning tool to identify small subsets of network partitions that are significant from the perspective of stochastic block model inference.
- module-coupling-metrics — Compute module coupling metrics for Python projects.
- modulename — this is a description
- mofax — Work with MOFA+ models in Python
- MOFF — Modular prediction of off-target effects for CRISPR/Cas9 system
- mofpy — Python package to handle MOF structures and perform various analysis.
- moftransformer — moftransformer
- mojoRPG — Mojo RPG test game
- mol-coma — Choi, J., Seo, S. & Park, S. COMA: efficient structure-constrained molecular generation using contractive and margin losses. J Cheminform 15, 8 (2023). https://doi.org/10.1186/s13321-023-00679-y
- mol2vec — Mol2vec - an unsupervised machine learning approach to learn vector representations of molecular substructures
- molgri — Generate molecular pseudotrajectories based on rotational grids.
- molmap — MolMap: An Efficient Convolutional Neural Network Based Molecular Deep Learning Tool
- molmapnets — Neural networks for MolMap generated features
- Molml-tools — A collection of tools for molecular machine learning