Projects in Awesome Lists tagged with scikit-learn-api

https://github.com/scikit-garden/scikit-garden

A garden for scikit-learn compatible trees

forest machine-learning python scientific-computing scikit-learn-api tree

Last synced: 27 Nov 2024

https://github.com/zillow/quantile-forest

Quantile Regression Forests compatible with scikit-learn.

machine-learning prediction-intervals python quantile-regression quantile-regression-forests random-forest scikit-learn-api uncertainty-estimation

Last synced: 05 Apr 2025

https://github.com/localcascadeensemble/lce

Random Forest or XGBoost? It is Time to Explore LCE

classification data-science machine-learning python regression scikit-learn-api

Last synced: 13 Apr 2025

https://github.com/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).

classification consists machine-learning machine-learning-algorithms prediction python python-3 regression reinforcement-learning scikit-image scikit-learn scikit-learn-api scikit-learn-python scikit-model semi-supervised-learning sklearn supervised-learning unsupervised-learning vector

Last synced: 11 Jan 2025

https://github.com/sktime/skbase

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

framework python scikit-learn-api skbase sktime

Last synced: 05 Apr 2025

https://github.com/ksachdeva/scikit-nni

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

automl hyperparameter-search hyperparameters neural-network-intelligence nni scikit-learn scikit-learn-api sklearn sklearn-library tool

Last synced: 09 Feb 2025

https://github.com/jlgarridol/sslearn

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

classification-algorithm machine-learning scikit-learn scikit-learn-api semi-supervised semi-supervised-learning semisupervised-learning

Last synced: 13 Jan 2025

https://github.com/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.

deep-neural-networks keras mysql numpy pandas python3 scikit-learn-api tensorflow time-series-analysis yfinance-api