ridge-regression

There are 2 repositories under ridge-regression topic.

facebookexperimental / Robyn
Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
marketing-mix-modeling marketing-mix-modelling mmm marketing-science econometrics adstocking cost-response-curve budget-allocation hyperparameter-optimization evolutionary-algorithm ridge-regression gradient-based-optimisation
Language:Jupyter Notebook 1035
JuliaStats / MultivariateStats.jl
A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)
julia multivariate-statistics dimensionality-reduction ica lda mds pca statistics regression cca isotonic-regression linear-regression ols-regression ridge-regression
Language:Julia 368
YiranJing / Coronavirus-Epidemic-COVID-19
👩🏻‍⚕️Covid-19 estimation and forecast using statistical model; 新型冠状病毒肺炎统计模型预测 (Jan 2020)
statistical-models visualization python coronavirus epidemic-model covid-19 seir-model gradient-descent ridge-regression
Language:Jupyter Notebook 243
machine-learning
je-suis-tm / machine-learning
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
naive-bayes support-vector-machine stochastic-gradient-descent batch-gradient-descent newton-method multinomial-naive-bayes linear-discriminant-analysis sequential-minimal-optimization k-nearest-neighbors dbscan expectation-maximization expectation-maximization-algorithm factor-analysis independent-component-analysis latent-semantic-analysis low-rank-approximation singular-value-decomposition ridge-regression lasso-regression em-algorithm
Language:Jupyter Notebook 204
Somnibyte / MLKit
A simple machine learning framework written in Swift 🤖
regression ridge-regression swift machine-learning machine-learning-algorithms artificial-intelligence polynomial-regression linear-regression machine-learning-library mlkit kmeans-clustering neural-network feedforward-neural-network backpropagation lasso-regression kmeans genetic-algorithm
Language:Swift 152
MBKraus / Predicting_real_estate_prices_using_scikit-learn
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)
predicting-housing-prices real-estate machine-learning python knn knn-regression lasso-regression lasso ridge-regression decision-trees random-forest neural-network mlp-regressor ols polynomial-regression amsterdam multi-layer-perceptron xgboost polynomial ensemble-learning
Language:Python 148
MLWithPytorch
Mayurji / MLWithPytorch
Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch
pytorch-implementation pytorch machine-learning-algorithms python3 kmeans-clustering linear-regression logistic-regression decision-trees naive-bayes-classifier svm-classifier pca-analysis tfidf-vectorizer naive-bayes-algorithm knn-classification gaussian-mixture-models lasso-regression ridge-regression machine-learning adaboost-algorithm
Language:Python 124
bhushan23 / ADMM
Implemented ADMM for solving convex optimization problems such as Lasso, Ridge regression
convex-optimization admm lasso-regression ridge-regression pytorch
Language:Jupyter Notebook 123
agrawal-priyank / machine-learning-regression
Built house price prediction model using linear regression and k nearest neighbors and used machine learning techniques like ridge, lasso, and gradient descent for optimization in Python
linear-regression multiple-regression house-price-prediction ridge-regression polynomial-regression gradient-descent python machine-learning machine-learning-regression coursera university-of-washington
Language:Jupyter Notebook 81
JuliaAI / MLJLinearModels.jl
Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)
regressions julia-language mlj sparse-regression lasso-regression ridge-regression robust-regression elastic-net logistic-regression multinomial-regression regression-models
Language:Julia 80
mTRF-Toolbox
mickcrosse / mTRF-Toolbox
A MATLAB package for modelling multivariate stimulus-response data
bci bmi brain-computer-interface brain-machine-interface neural-decoding neural-interface ridge-regression stimulus-response-correlation strf system-identification temporal-response-function trf
Language:MATLAB 67
models-by-example
m-clark / models-by-example
By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.
algorithms bayesian maximum-likelihood-estimation linear-regression logistic-regression neural-network zero-inflated mixed-models hamiltonian-monte-carlo lasso ridge-regression penalized-regression gradient-descent splines rkhs pca cox-model expectation-maximization machine-learning gaussian-processes
Language:R 65
englianhu / binary.com-interview-question
次元期权应征面试题范例。
bayesian investment betting statistics lasso-regression ridge-regression elastic-net mcmc garch ets arima kelly-criterion investment-portfolio investment-strategies markowitz-portfolio acd
Language:HTML 57
SravB / Computer-Vision-Weightlifting-Coach
Analyzes weightlifting videos for correct posture using pose estimation with OpenCV
computer-vision machine-learning lasso-regression ridge-regression random-forest elastic-net logistic-regression weightlifting keypoint-detection pose-estimation mpii-dataset ensemble-learning opencv openpose
Language:Jupyter Notebook 36
getzze / RobustModels.jl
A Julia package for robust regressions using M-estimators and quantile regressions
julia-language robust-regression quantile-regression ridge-regression m-estimator mm-estimator s-estimator tau-estimator expectile-regression mquantile-regression outlier-detection outlier-rejection
Language:Julia 34
shubhpawar / Automated-Essay-Scoring
Automated Essay Scoring on The Hewlett Foundation dataset on Kaggle
natural-language-processing machine-learning automatic-essay-scoring linear-regression ridge-regression lasso-regression support-vector-regression gradient-boosting-regressor nltk sklearn ensemble-learning cohens-kappa text-regression text-processing text-analytics
Language:Jupyter Notebook 32
buus2 / dcbit
Demo from Data Community Bydgoszcz i Toruń, 27.02.2019
machine-learning machine-learning-algorithms lightgbm sklearn ridge-regression linear-regression pandas pandas-profiling
Language:Jupyter Notebook 30
lightonai / double-descent-curve
Double Descent Curve with Optical Random Features
machine-learning optical-network photonic-computing random-features ridge-regression
Language:Jupyter Notebook 27
faizanxmulla / CS2007-machine-learning-techniques
Repository containing introduction to the main methods and models used in machine learning problems of regression, classification and clustering.
classification clustering regression decision-trees iitmadrasonlinedegree knn lasso-regression least-square-regression naive-bayes-classifier neural-network perceptron random-forest ridge-regression
Language:Jupyter Notebook 24
Nemshan / predicting-Paid-amount-for-Claims-Data
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.
python predictive-modeling ridge-regression lasso-regression random-forest earth
Language:Jupyter Notebook 20
alexvlis / movie-recommendation-system
Movie Recommendation System using the MovieLens dataset
machine-learning collaborative-filtering neural-network svm ridge-regression deep-learning movie-recommendation movielens-dataset
Language:Python 19
docnok / detection-estimation-learning
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.
detection estimation-theory machine-learning python python-notebook jupyter-notebook teaching-materials estimation regression kalman-filter hypothesis-testing signal-processing ridge-regression lasso lasso-regression tikhonov-regularization maximum-likelihood
Language:Jupyter Notebook 19
NikhilaThota / CapstoneProject_House_Prices_Prediction
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.
python ridge-regression linear-regression data-visualization data-analysis datawrangling predictive-modeling machine-learning correlation standardization data-extraction data-exploration encoding-library regression-analysis lasso-regression regularization root-mean-squared-error-metric parameter-tuning cross-validation k-fold
Language:Jupyter Notebook 19
schatzlab / scikit-ribo
Accurate estimation and robust modelling of translation dynamics at codon resolution
riboseq glm random-forest ridge-regression glmnet
Language:Python 18
SteffenMoritz / ridge
CRAN R Package: Ridge Regression with automatic selection of the penalty parameter
cran ridge-regression regression
Language:R 18
mdl-complexity
csinva / mdl-complexity
MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".
complexity linear-models linear-regression machine-learning ai statistics linear-regression-models artificial-intelligence mdl minimum-description-length double-descent mean-squared-error bias-variance-tradeoff bias-variance-trade ridge-regression model-selection information-theory
Language:Jupyter Notebook 17
faosorios / fastmatrix
Fast computation of some matrices useful in statistics
statistics sweep-operator matrix-norms power-method sherman-morrison commutation-matrix matrix-api-for-r-packages lu-factorization symmetrizer-matrix cran ols-regression ridge-regression r-package jarque-bera-test ldl-factorization modified-cholesky
Language:C 17
Kennethborup / self_distillation
Self-Distillation with weighted ground-truth targets; ResNet and Kernel Ridge Regression
kernel-ridge-regression distillation knowledge-distillation self-distillation resnet ridge-regression self-training
Language:Jupyter Notebook 17
mlesnoff / rchemo
Archived repo (see Readme) - R package for regression and discrimination, with special focus on chemometrics and high-dimensional data.
dimension-reduction discrimination chemometrics knn regression-models lwplsr kernel-methods ridge-regression pca partial-least-squares-regression non-linear-regression plsda latent-variable-models lwplsda svm signal-processing ridge-discrimination kernel-pca fda spectroscopy
Language:R 14
tweichle / Predicting-Baseball-Statistics
Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn
machine-learning data-visualization supervised-learning predictive-modeling exploratory-data-analysis logistic-regression regularization cross-validation python data-science oversampling decision-trees random-forests support-vector-machines xgboost linear-regression ridge-regression lasso-regression elastic-net k-nearest-neighbors
Language:Jupyter Notebook 14
regularized-linear-regression-deep-dive
wyattowalsh / regularized-linear-regression-deep-dive
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.
regularized-linear-regression lasso-regression ridge-regression elastic-net-regression coordinate-descent optimization python jupyter-notebook ordinary-least-squares regularization regression
Language:Jupyter Notebook 13
sandipanpaul21 / Logistic-regression-in-python
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
logistic-regression regularization l1-regularization l2-regularization lasso-regression ridge-regression elastic-net-regression multiclass-logistic-regression weight-of-evidence information-value logistic-regression-assumptions
Language:Jupyter Notebook 12
ekapope / Predicting-condominium-price-using-data-from-webscraping
Scrape Bangkok condominium listing from hipflat.com and compare ML performance
housing webscraping ridge-regression random-forest gradient-boosting
Language:HTML 11
hanfang / glmnet_py
glmnet for python
glmnet ridge-regression lasso elasticnet
Language:Python 11
mdpetters / RegularizationTools.jl
A Julia package to perform Tikhonov regularization for small to moderate size problems.
inverse-problems regularization-methods ridge-regression tikhonov-regularization
Language:Julia 11
spiningup / Machine-learning-methods-for-materials-science
Evaluate various supervised learning methods to predict cohesive energies of solids (kernel ridge regression is the best)
regression machine-learning-algorithms ridge-regression material-design
Language:Python 11

ridge-regression

facebookexperimental / Robyn

JuliaStats / MultivariateStats.jl

YiranJing / Coronavirus-Epidemic-COVID-19

je-suis-tm / machine-learning

Somnibyte / MLKit

MBKraus / Predicting_real_estate_prices_using_scikit-learn

Mayurji / MLWithPytorch

bhushan23 / ADMM

agrawal-priyank / machine-learning-regression

JuliaAI / MLJLinearModels.jl

mickcrosse / mTRF-Toolbox

m-clark / models-by-example

englianhu / binary.com-interview-question

SravB / Computer-Vision-Weightlifting-Coach

getzze / RobustModels.jl

shubhpawar / Automated-Essay-Scoring

buus2 / dcbit

lightonai / double-descent-curve

faizanxmulla / CS2007-machine-learning-techniques

Nemshan / predicting-Paid-amount-for-Claims-Data

alexvlis / movie-recommendation-system

docnok / detection-estimation-learning

NikhilaThota / CapstoneProject_House_Prices_Prediction

schatzlab / scikit-ribo

SteffenMoritz / ridge

csinva / mdl-complexity

faosorios / fastmatrix

Kennethborup / self_distillation

mlesnoff / rchemo

tweichle / Predicting-Baseball-Statistics

wyattowalsh / regularized-linear-regression-deep-dive

sandipanpaul21 / Logistic-regression-in-python

ekapope / Predicting-condominium-price-using-data-from-webscraping

hanfang / glmnet_py

mdpetters / RegularizationTools.jl

spiningup / Machine-learning-methods-for-materials-science