mlr
There are 52 repositories under mlr topic.
shenweichen/DeepCTR
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
mlr-org/mlr
Machine Learning in R
mlr-org/mlrMBO
Toolbox for Bayesian Optimization and Model-Based Optimization in R
QikaiXu/Recommender-System-Pytorch
基于 Pytorch 实现推荐系统相关的算法
Hirosora/LightCTR
LightCTR is a tensorflow 2.0 based, extensible toolbox for building CTR/CVR predicting models.
Coorsaa/shinyMlr
shiny-mlr: Integration of the mlr package into shiny
jakob-r/mlrHyperopt
Easy Hyper Parameter Optimization with mlr and mlrMBO.
chen0040/js-regression
Package provides javascript implementation of linear regression and logistic regression
redichh/ShapleyR
Package for a nice and smoothe usage of the shapley value for mlr
mlr-archive/mlr-tutorial
The mlr package online tutorial
mlr-org/mlr3filters
Filter-based feature selection for mlr3
rgmantovani/mtlSuite
Meta-learning basic suite for machine learning experiments.
UnixJunkie/omlr
OCaml wrapper on top of R to perform Multiple Linear Regression
gabrielcrepeault/xgbmr
Micro-reserve model using XGBoost
hita03/Derby-Horse-Racing
Big Data Derby Racing Dataset's Analysis Project
ndleah/transactions
🪙 Linear regression model, predict monthly transaction amount
shramkoartem/nsga3
R implementation of the Non-dominated Sorting Genetic Algorithm III for multi objective feature selection
vaitybharati/Assignment-05-Multiple-Linear-Regression-2
Assignment-05-Multiple-Linear-Regression-2. Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few years Marketing Spend -- spend on Marketing in the past few years State -- states from which data is collected Profit -- profit of each state in the past few years.
moritzkoerber/ensemble_machine_learning_comparison
A comparison of various ensemble machine learning algorithms (XGboost, random forest, ranger) to predict accelerometers
AlessioChen/Multi-Class-Logistic-Regression-with-Optimization-Methods
Implementation of Trust Region and Gradient Descent methods for Multinomial Regression
andrewcparnell/intro_to_ml
An introductory machine learning course of 1-2 hours
j-b-ferguson/covid-19-victoria-regression-analysis
Using regression analysis to create a prediction model to forecast Victorian COVID-19 cases.
Prem-98/Multi-linear-regression
MLR assignment
sevak-crypto/MLR
multiple linear regression code with examples in python and JS
sleepbysleep/variable_selection
Variable selection for NIR spectral analysis(regression and classification) based on WRC, VIP, SFS, and SPA
drnitinmalik/multiple-linear-regression
Predicting net yearly revenue of Top 50 US startups on the basis of their financial data.
embarbos/air-pollution-data-analysis
Final Project for STA 135 with Dr. Xiucai Ding
IshitaBharadwaj/Derby-Horse-Racing-DA
Big Data Derby Racing Dataset's Analysis Project
matthewfishermv/MachineLearning-with-R
Machine Learning algorithms in R
meaganng/microclimate
Climate change is a key factor in how extreme weather events affect how ecosystems and species react to these changes in temperatures. University of British Columbia's (UBC) Botanical Garden is interested in improving microclimate information within the garden to understand how areas with shade create respite zones for species. Due to the recent extreme weather temperatures in Vancouver, the garden is interested in how to continue to adapt and mitigate to these extremes. Microclimates are important as they are cooler temperatures beneath the canopy. Looking at how canopy cover influences land surface temperature can give insight on microclimates. Using LiDAR metrics to calculate canopy cover and Landsat to calculate land surface temperature, a model was built to understand the significance of canopy cover and land surface temperature, with the addition of other LiDAR metrics. The model could only determine a 34% variation between the variables tested. Canopy cover showed to have a p-value of 0.0993 and maximum height had a p-value of 0.0034. To investigate the results further, an unpaired t-test was run to determine the relationship between areas with canopy cover and areas without canopy cover. The t-test showed there are significant differences as the p-value was 0.0035. With the results, they provide observations of how canopy cover currently influences microclimate within the garden. Areas found to have a high percentage of canopy cover reflected lower land surface temperatures. Currently, the model has the structure to predict canopy cover with LiDAR metrics. However, finer data is needed to accurately predict microclimate. Recommendations are provided to enhance the study area with future directions for research within UBC Botanical Garden to conduct a more intricate analysis.
PatilSukanya/Assignment-05.-Multiple-Linear-regression-Q2
Used libraries and functions as follows:
SnowyPainter/refactored-engine
선물 지수와 주가의 상관 관계에 대한 연구와 분석
tissyamalik/mlr-boston-house-pricing
Predicting House Price on Boston dataset using Multiple Linear Regression model
yuliyamkh/sleep-disorder-prediction
Sleep Disorder Prediction with Multinomial Logistic Regression
Bonniface/Study-Materials
Where I keep my Codes for the New thing I learn.
Himnish/churn-prediction-analysis
Using a telecom company's data of services provided to customers and observing how customers use it to predict if they will decide to continue or cease to be a customer of the company.