Topic: lasso-regression Goto Github
Some thing interesting about lasso-regression
Some thing interesting about lasso-regression
lasso-regression,An extension of sklearn's Lasso/ElasticNet/Ridge model to allow users to customize the penalties of different covariates. Works similar to penalty.factor parameter in R's glmnet.
User: 3zhang
lasso-regression,Harvard Project - Accuracy improvement by adding seasonality premium pricing
User: amayumradia
Home Page: https://amay-umradia-tr3t.squarespace.com/
lasso-regression,Machine-Learning-Regression
User: amitha353
lasso-regression,Nonparametric regression and prediction using the highly adaptive lasso algorithm
User: benkeser
lasso-regression,This repository shows how Lasso Regression selects correlated predictors
User: bhattbhavesh91
lasso-regression,Implemented ADMM for solving convex optimization problems such as Lasso, Ridge regression
User: bhushan23
lasso-regression,Implementation of Relaxed Lasso Algorithm for Linear Regression.
Organization: continental
Home Page: https://relaxedlasso.readthedocs.io/
lasso-regression,TwitPersonality: Computing Personality Traits from Tweets using Word Embeddings and Supervised Learning
Organization: d2klab
lasso-regression,Environmental Studies (P/F course) - End Semester Project
User: deepthisudharsan
lasso-regression,Jupyter notebook that outlines the process of creating a machine learning predictive model. Predicts the peak "Wins Shared" by the current draft prospects based on numerous features such as college stats, projected draft pick, physical profile and age. I try out multiple models and pick the best performing one for the data from my judgement.
User: dgrubis
lasso-regression,Python notebooks for my graduate class on Detection, Estimation, and Learning. Intended for in-class demonstration. Notebooks illustrate a variety of concepts, from hypothesis testing to estimation to image denoising to Kalman filtering. Feel free to use or modify for your instruction or self-study.
User: docnok
lasso-regression,Integrating LASSO and bootstrapping algorithm to find best prognostic or predictive biomarkers
User: dongqiangzeng0808
lasso-regression,次元期权应征面试题范例。
User: englianhu
Home Page: https://gitee.com/englianhu
lasso-regression,Repository containing introduction to the main methods and models used in machine learning problems of regression, classification and clustering.
User: faizanxmulla
lasso-regression,Roger Ebert's movie ratings prediction
User: gabrielcs
lasso-regression,Machine learning algorithms in Dart programming language
User: gyrdym
Home Page: https://gyrdym.github.io/ml_algo/
lasso-regression,The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure-activity relationships (QSAR). Herein, we developed QSAR models that quantitatively predict both thermodynamic and kinetic-based binding parameters of small molecules and the HIV-1 TAR model system. Small molecules bearing diverse scaffolds was screened against the HIV-1 TAR using surface plasmon resonance. Then multiple linear regression (MLR) combined with feature selection was performed to afford robust models that allowed direct interpretation of properties critical for both binding strength and kinetic rate constants. These models were externally validated with new molecules and their accurate performance confirmed via comparison to ensemble tree methods.
User: hargrove-lab
lasso-regression,MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3
User: hiroyuki-kasai
lasso-regression,Applied Machine Learning
User: hkiang01
lasso-regression,LASSO Regularization in C++
User: j3fall
lasso-regression,Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression
User: je-suis-tm
Home Page: https://je-suis-tm.github.io/machine-learning
lasso-regression,Analysis of NBA player stats and salaries of the 2016-17 for the 17-18 season
User: joeyism
Home Page: http://nnnba.herokuapp.com
lasso-regression,Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)
Organization: juliaai
lasso-regression,Hyperspectral image Processing and Classification. This repository provides my solution for Project assignment for the course of Machine Learning and Computational Statistics for the MSc in Data Science at Athens University of Economics and Business.
User: konkyrkos
lasso-regression,This repository contains only projects using regression analysis techniques. Examples include a comprehensive analysis of retail store expansion strategies using Lasso and Ridge regressions.
User: marcotav
lasso-regression,Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch
User: mayurji
lasso-regression,Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)
User: mbkraus
lasso-regression,In this project, I applied different regression models for rmse and mae on antenna dataset for predict signal strength.
User: mhassaanbutt
lasso-regression,Sequential adaptive elastic net (SAEN) approach, complex-valued LARS solver for weighted Lasso/elastic-net problems, and sparsity (or model) order detection with an application to single-snapshot source localization.
User: mntabassm
lasso-regression,An R package for modern methods for non-probability surveys
Organization: ncn-foreigners
Home Page: https://ncn-foreigners.github.io/nonprobsvy/
lasso-regression,Introduction The context is the 2016 public use NH medical claims files obtained from NH CHIS (Comprehensive Health Care Information System). The dataset contains Commercial Insurance claims, and a small fraction of Medicaid and Medicare payments for dually eligible people. The primary purpose of this assignment is to test machine learning (ML) skills in a real case analysis setting. You are expected to clean and process data and then apply various ML techniques like Linear and no linear models like regularized regression, MARS, and Partitioning methods. You are expected to use at least two of R, Python and JMP software. Data details: Medical claims file for 2016 contains ~17 millions rows and ~60 columns of data, containing ~6.5 million individual medical claims. These claims are all commercial claims that were filed by healthcare providers in 2016 in the state of NH. These claims were ~88% for residents of NH and the remaining for out of state visitors who sought care in NH. Each claim consists of one or more line items, each indicating a procedure done during the doctor’s visit. Two columns indicating Billed amount and the Paid amount for the care provided, are of primary interest. The main objective is to predict “Paid amount per procedure” by mapping a plethora of features available in the dataset. It is also an expectation that you would create new features using the existing ones or external data sources. Objectives: Step 1: Take a random sample of 1 million unique claims, such that all line items related to each claim are included in the sample. This will result in a little less than 3 million rows of data. Step 2: Clean up the data, understand the distributions, and create new features if necessary. Step 3: Run predictive models using validation method of your choice. Step 4: Write a descriptive report (less than 10 pages) describing the process and your findings.
User: nemshan
lasso-regression,Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.
User: nikhilathota
lasso-regression,Predicting the compressive strength of concrete using ML methods and Artificial Nueral Networks. Tools used in this project are Jupyter Notebook, UCI ML repository,Kaggle,Google colab.
User: nitya123-github
lasso-regression,To know internal working of machine learning algorithms, I have implemented types of regression through scratch.
User: oprishri
lasso-regression,Logistic Regression technique in machine learning both theory and code in Python. Includes topics from Assumptions, Multi Class Classifications, Regularization (l1 and l2), Weight of Evidence and Information Value
User: sandipanpaul21
lasso-regression,A repository for a machine learning project about developing a hybrid movie recommender system.
User: sebastianrokholt
lasso-regression,Automated Essay Scoring on The Hewlett Foundation dataset on Kaggle
User: shubhpawar
lasso-regression,Capstone Project Gold Price Prediction using Machine learning Approach for Udacity Machine Learning engineer Nanodegree Program
User: sid321axn
lasso-regression,A simple machine learning framework written in Swift 🤖
User: somnibyte
lasso-regression,Analyzes weightlifting videos for correct posture using pose estimation with OpenCV
User: sravb
lasso-regression,For quick search
User: ssq
lasso-regression,Evaluating multiple classifiers after SVM-RFE (Support Vector Machine-Recursive Feature Elimination)
User: tanerarslan
lasso-regression,🤠 📿 The Highly Adaptive Lasso
Organization: tlverse
Home Page: https://tlverse.org/hal9001
lasso-regression,Drop-in replacement of sklearn's Linear Regression with coefficients constraints
User: tsitsimis
Home Page: https://tsitsimis.github.io/constrainedlr/
lasso-regression,Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn
User: tweichle
lasso-regression,Explanations and Python implementations of Ordinary Least Squares regression, Ridge regression, Lasso regression (solved via Coordinate Descent), and Elastic Net regression (also solved via Coordinate Descent) applied to assess wine quality given numerous numerical features. Additional data analysis and visualization in Python is included.
User: wyattowalsh
lasso-regression,A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.
Organization: yaglm
lasso-regression,Iterative shrinkage / thresholding algorithms (ISTAs) for linear inverse problems
User: yunhui-gao
lasso-regression,Conditional Auto-Regressive LASSO in R
User: yunyishen
Home Page: https://yunyishen.github.io/CAR-LASSO
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