Topic: scikit-learn-api Goto Github
Some thing interesting about scikit-learn-api
Some thing interesting about scikit-learn-api
scikit-learn-api,The code commited while the code tutorials on yt.
User: 4rund3v
scikit-learn-api,A python implementation of the Generative Topographic Mapping
User: amaotone
scikit-learn-api,Scikit-learn (sklearn) projects in form of Jupyter Notebooks
User: architadesai
scikit-learn-api,Python wrapper around R's lovely `smooth.spline`
User: ceholden
scikit-learn-api,A sample of often unknown and underrated functionalities in scikit learn library.
User: davisy
scikit-learn-api,Machine Learning project to predict popularity of Instagram posts
User: dayeonhwang
scikit-learn-api,A package for fitting regularized models from scikit-learn via proximal gradient descent
User: jameschapman19
Home Page: https://scikit-prox.readthedocs.io/en/latest/
scikit-learn-api,24/01/2024 Jeyfrey J. Calero R. Aplicación de Redes Neuronales con scikit-learn streamlit, pandas, seaborn y matplolib
User: jeyjocar
Home Page: https://redesneuronales.streamlit.app/
scikit-learn-api,The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.
User: jlgarridol
Home Page: https://jlgarridol.github.io/sslearn/
scikit-learn-api,Pipelines transformMixin that preserve the format dataframe and automation in correlation
User: kaladabrio2020
scikit-learn-api,This repository contains the machine learning examples in anaconda-python
User: kodtodya
scikit-learn-api,AutoML - Hyper parameters search for scikit-learn pipelines using Microsoft NNI
User: ksachdeva
scikit-learn-api,Notes, tutorials, code snippets and templates focused on scikit-learn API extention for Machine Learning
User: kyaiooiayk
scikit-learn-api,Random Forest or XGBoost? It is Time to Explore LCE
User: localcascadeensemble
Home Page: https://lce.readthedocs.io/
scikit-learn-api,Scikit-klearn compatible BinaryEncoder class capable of handling unseen categories in an automated fashion
User: magnusax
scikit-learn-api,Contains models implemented from scratch and a project implemented from end-to-end
User: manikumarreddy35
scikit-learn-api,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.
User: myoussef885
scikit-learn-api,A scikit-learn compatible implementation of Bumping as described by “Elements of Statistical Learning” second edition (290-292).
User: pr38
scikit-learn-api,Gender Classifier, Price Predictor, Human Behavior Predictor and other Insights from Machine Learning.
User: rakshithvasudev
scikit-learn-api,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).
User: reddyprasade
scikit-learn-api,classify anyone as either 'male' or 'female' given just their 'height', 'weight' and 'shoe size' (youtube challenge by 'Siraj Raval')
User: ryhansunny
scikit-learn-api,A garden for scikit-learn compatible trees
Organization: scikit-garden
Home Page: http://scikit-garden.github.io/
scikit-learn-api,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.
User: shivamgupta92
scikit-learn-api,
User: shivendra690
scikit-learn-api,Hierarchical Multi Class validation metrics:HMC-loss
User: suzuki-shm
scikit-learn-api,The transformer that transforms data so to squared norm of transformed data becomes Mahalanobis' distance.
User: tetutaro
scikit-learn-api,Quantile Regression Forests compatible with scikit-learn.
Organization: zillow
Home Page: https://zillow.github.io/quantile-forest/
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