scikit-learn-api

There are 27 repositories under scikit-learn-api topic.

  • scikit-garden/scikit-garden

    A garden for scikit-learn compatible trees

    Language:Python281136575

  • zillow/quantile-forest

    Quantile Regression Forests compatible with scikit-learn.

    Language:Python164111318

  • LocalCascadeEnsemble/LCE

    Random Forest or XGBoost? It is Time to Explore LCE

    Language:Python66598
  • Machine-Learning-with-Scikit-Learn-Python-3.x

    reddyprasade/Machine-Learning-with-Scikit-Learn-Python-3.x

    In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).

    Language:Jupyter Notebook523023

  • ksachdeva/scikit-nni

    AutoML - Hyper parameters search for scikit-learn pipelines using Microsoft NNI

    Language:Python22103

  • dayeonhwang/instagramPredictor

    Machine Learning project to predict popularity of Instagram posts

    Language:Python17406

  • sktime/skbase

    Base classes for creating scikit-learn-like parametric objects, and tools for working with them.

    Language:Python154819

  • jlgarridol/sslearn

    The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.

    Language:Python7870

  • rakshithvasudev/Machine-Learning

    Gender Classifier, Price Predictor, Human Behavior Predictor and other Insights from Machine Learning.

    Language:Jupyter Notebook7207

  • jameschapman19/scikit-prox

    A package for fitting regularized models from scikit-learn via proximal gradient descent

    Language:Python4300

  • amaotone/pygtm

    A python implementation of the Generative Topographic Mapping

    Language:Python2633

  • pr38/socraticbumpsearch

    A scikit-learn compatible implementation of Bumping as described by “Elements of Statistical Learning” second edition (290-292).

    Language:Python2100

  • architadesai/scikit-learn-projects

    Scikit-learn (sklearn) projects in form of Jupyter Notebooks

    Language:Jupyter Notebook1200

  • Jeyjocar/Redes-Neuronales

    24/01/2024 Jeyfrey J. Calero R. Aplicación de Redes Neuronales con scikit-learn streamlit, pandas, seaborn y matplolib

    Language:Python110

  • magnusax/BinaryEncoder

    Scikit-klearn compatible BinaryEncoder class capable of handling unseen categories in an automated fashion

    Language:Python0100

  • ManiKumarReddy35/Machine-Learning

    Contains models implemented from scratch and a project implemented from end-to-end

    Language:Jupyter Notebook0100

  • MYoussef885/Breast_Cancer_Classification_using_NN

    The "Breast Cancer Classification using Neural Networks" project focuses on predicting the presence of breast cancer using deep learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn, Matplotlib, and implementing neural networks.

    Language:Jupyter Notebook00

  • RyhanSunny/python-GENDERCLASSIFIER_Machine_Learning_AI

    classify anyone as either 'male' or 'female' given just their 'height', 'weight' and 'shoe size' (youtube challenge by 'Siraj Raval')

    Language:Python00

  • Shivendra690/Automated_image_captioning

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  • 4rund3v/data_science_tutorial

    The code commited while the code tutorials on yt.

    Language:Jupyter Notebook20

  • ceholden/pysmoothspl

    Python wrapper around R's lovely `smooth.spline`

    Language:Python411

  • Davisy/14-Lesser-Known-Impressive-Features-in-Scikit-learn-Library

    A sample of often unknown and underrated functionalities in scikit learn library.

    Language:Jupyter Notebook201

  • kaladabrio2020/my-pipelines-sklearn

    Pipelines transformMixin that preserve the format dataframe and automation in correlation

    Language:Jupyter Notebook

  • kodtodya/machine-learning-examples

    This repository contains the machine learning examples in anaconda-python

    Language:Python10

  • kyaiooiayk/Scikit-Learn-Notes

    Notes, tutorials, code snippets and templates focused on scikit-learn API extention for Machine Learning

    Language:Jupyter Notebook

  • suzuki-shm/hmc_loss

    Hierarchical Multi Class validation metrics:HMC-loss

    Language:Python

  • tetutaro/mahalanobis_transformer

    The transformer that transforms data so to squared norm of transformed data becomes Mahalanobis' distance.

    Language:Jupyter Notebook30