scikitlearn
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scikitlearn  TFLearn  

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48,142  9,570  
0.8%  0.0%  
9.9  0.0  
3 days ago  11 months ago  
Python  Python  
BSD 3clause "New" or "Revised" License  MIT License 
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scikitlearn

Data Science toolset summary from 2021
Scikitlearn  It is one of the most widely used frameworks for Python based Data science tasks. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, kmeans and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Link  https://scikitlearn.org/

Intel Extension for ScikitLearn
Hi all,
Currently some works is being done to improve computational primitives of scikitlearn to enhance its overhaul performances natively.
You can have a look at this exploratory PR: https://github.com/scikitlearn/scikitlearn/pull/20254
This other PR is a clear revamp of this previous one:

ScikitLearn Version 1.0
Just to clarify, scikitlearn 1.0 has not been released yet. The latest tag in the github repo is 1.0.rc2
https://github.com/scikitlearn/scikitlearn/releases/tag/1....

Top 10 Python Libraries for Machine Learning
Website: https://scikitlearn.org/ Github Repository: https://github.com/scikitlearn/scikitlearn Developed By: SkLearn.org Primary Purpose: Predictive Data Analysis and Data Modeling

where is binary_metric function in sklearn package
There is a function named binary_metric in https://github.com/scikitlearn/scikitlearn/blob/main/sklearn/metrics/_base.py

Use ScikitLearn and Runflow
If you're not familiar with Scikitlearn and Runflow,

Confused as to what exaclty a piece of code does
well you can start at https://github.com/scikitlearn/scikitlearn/blob/main/sklearn/model_selection/_validation.py, or maybe someone will guide you later

What Makes Python Libraries So Important For Data Science Learning?
Next comes the complexity of drawing the maximum possible number of valuable insights. Using different python libraries such as ScikitLearn, PyTorch, Pandas, etc., complications of data analysis can be solved within a minute. And the complexity associated with visualisation gets handled by other data visualisation libraries like Matploitlib, PyTorch, etc.

Is there a way to map cluster centers back to a dataframe?
To avoid the issue with convergence (and the discrepancy between the labels_ and cluster_centers_), you can set tol=0, though this can of course lead to issues if convergence is a problem. There was an issue about it here. Assuming it's converged, then the order is fine.

Any from scratch Hamming Loss implementations?
The source code for the function you refer to is quite straightforward anyway. The definition of count_nonzero() is here.
TFLearn

Base ball
Both the teams in a game are given their individual ID values and are made into vectors. Relevant data like the home and away team, home runs, RBI’s, and walk’s are all taken into account and passed through layers. There’s no need to reinvent the wheel here, there's a multitude of libraries that enable a coder to implement machine learning theories efficiently. In this case we will be using a library called TFlearn, documentation available from http://tflearn.org. The program will output the home and away teams as well as their respective score predictions.
What are some alternatives?
Keras  Deep Learning for humans
Surprise  A Python scikit for building and analyzing recommender systems
Prophet  Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or nonlinear growth.
tensorflow  An Open Source Machine Learning Framework for Everyone
gensim  Topic Modelling for Humans
PyBrain
MLflow  Open source platform for the machine learning lifecycle
seqeval  A Python framework for sequence labeling evaluation(namedentity recognition, pos tagging, etc...)
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), KMeans, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
xgboost  Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow