scikit-learn-api | Topic | Ecosyste.ms: Repos

Topic: "scikit-learn-api"

scikit-garden/scikit-garden

A garden for scikit-learn compatible trees

Language: Python - Size: 320 KB - Last synced at: 3 days ago - Pushed at: 10 months ago - Stars: 286 - Forks: 75

zillow/quantile-forest

Quantile Regression Forests compatible with scikit-learn.

Language: Python - Size: 75.7 MB - Last synced at: 3 days ago - Pushed at: 6 days ago - Stars: 227 - Forks: 29

LocalCascadeEnsemble/LCE

Random Forest or XGBoost? It is Time to Explore LCE

Language: Python - Size: 300 KB - Last synced at: 12 days ago - Pushed at: over 1 year ago - Stars: 66 - Forks: 11

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 Notebook - Size: 72.8 MB - Last synced at: 21 days ago - Pushed at: almost 4 years ago - Stars: 55 - Forks: 25

sktime/skbase

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

Language: Python - Size: 1.38 MB - Last synced at: about 17 hours ago - Pushed at: 22 days ago - Stars: 25 - Forks: 15

ksachdeva/scikit-nni

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

Language: Python - Size: 11.7 MB - Last synced at: 6 days ago - Pushed at: over 2 years ago - Stars: 24 - Forks: 3

dayeonhwang/instagramPredictor

Machine Learning project to predict popularity of Instagram posts

Language: Python - Size: 4.88 KB - Last synced at: almost 2 years ago - Pushed at: over 7 years ago - Stars: 14 - Forks: 5

jlgarridol/sslearn

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

Language: Python - Size: 3.38 MB - Last synced at: 26 days ago - Pushed at: 4 months ago - Stars: 8 - Forks: 2

ShivamGupta92/Analysis-of-Market-Trends-using-deep-learning

Analysis of market trend using Deep Learning is project that forecasts stock prices using historical data and ML models. Leveraging data collection, feature engineering, and model training. Primarily designed for the Indian stock market, it is adaptable for international markets, providing valuable insights for investors and analysts.

Language: Jupyter Notebook - Size: 1.34 MB - Last synced at: 24 days ago - Pushed at: 9 months ago - Stars: 7 - Forks: 1

rakshithvasudev/Machine-Learning

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

Language: Jupyter Notebook - Size: 19.5 MB - Last synced at: about 2 years ago - Pushed at: over 5 years ago - Stars: 7 - Forks: 6

jameschapman19/scikit-prox

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

Language: Python - Size: 104 KB - Last synced at: 15 days ago - Pushed at: almost 2 years ago - Stars: 4 - Forks: 0

XuegongLab/neoguider

NeoGuider, neoepitope detection using advanced feature engineering

Language: Python - Size: 12.4 MB - Last synced at: 7 days ago - Pushed at: 8 days ago - Stars: 3 - Forks: 1

pr38/socraticbumpsearch

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

Language: Python - Size: 14.6 KB - Last synced at: 2 months ago - Pushed at: about 5 years ago - Stars: 2 - Forks: 0

amaotone/pygtm

A python implementation of the Generative Topographic Mapping

Language: Python - Size: 55.7 KB - Last synced at: about 1 year ago - Pushed at: over 6 years ago - Stars: 2 - Forks: 3

Jeyjocar/Redes-Neuronales

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

Language: Python - Size: 360 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

architadesai/scikit-learn-projects

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

Language: Jupyter Notebook - Size: 1.95 KB - Last synced at: about 2 years ago - Pushed at: about 6 years ago - Stars: 1 - Forks: 0

suzuki-shm/hmc_loss

Hierarchical Multi Class validation metrics:HMC-loss

Language: Python - Size: 21.5 KB - Last synced at: 19 days ago - Pushed at: about 8 years ago - Stars: 1 - Forks: 0

LatiefDataVisionary/scikit-learn-with-indonesia-belajar

Language: Jupyter Notebook - Size: 9.34 MB - Last synced at: 24 days ago - Pushed at: 24 days ago - Stars: 0 - Forks: 0

kaladabrio2020/my-pipelines-sklearn

Pipelines transformMixin that preserve the format dataframe and automation in correlation

Language: Jupyter Notebook - Size: 13.7 KB - Last synced at: 22 days ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

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 Notebook - Size: 134 KB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0