mlr
There are 1 repository under mlr topic.
- DeepCTR
shenweichen / DeepCTR
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
Language:Python 7372 - Language:R 1630
- Language:R 187
QikaiXu / Recommender-System-Pytorch
基于 Pytorch 实现推荐系统相关的算法
Language:Jupyter Notebook 143Hirosora / LightCTR
LightCTR is a tensorflow 2.0 based, extensible toolbox for building CTR/CVR predicting models.
Language:Python 103- Language:R 92
jakob-r / mlrHyperopt
Easy Hyper Parameter Optimization with mlr and mlrMBO.
Language:HTML 30chen0040 / js-regression
Package provides javascript implementation of linear regression and logistic regression
Language:JavaScript 29- Language:R 25
- Language:HTML 20
- mlr3filters
mlr-org / mlr3filters
Filter-based feature selection for mlr3
Language:R 19 rgmantovani / mtlSuite
Meta-learning basic suite for machine learning experiments.
Language:R 6UnixJunkie / omlr
OCaml wrapper on top of R to perform Multiple Linear Regression
Language:OCaml 6- Language:R 4
hita03 / Derby-Horse-Racing
Big Data Derby Racing Dataset's Analysis Project
Language:Jupyter Notebook 4ndleah / transactions
🪙 Linear regression model, predict monthly transaction amount
Language:R 4shramkoartem / nsga3
R implementation of the Non-dominated Sorting Genetic Algorithm III for multi objective feature selection
Language:R 3vaitybharati / 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.
Language:Jupyter Notebook 3moritzkoerber / ensemble_machine_learning_comparison
A comparison of various ensemble machine learning algorithms (XGboost, random forest, ranger) to predict accelerometers
Language:R 2andrewcparnell / intro_to_ml
An introductory machine learning course of 1-2 hours
Language:TeX 1j-b-ferguson / covid-19-victoria-regression-analysis
Using regression analysis to create a prediction model to forecast Victorian COVID-19 cases.
Language:R 1Prem-98 / Multi-linear-regression
MLR assignment
Language:Jupyter Notebook 1sevak-crypto / MLR
multiple linear regression code with examples in python and JS
Language:JavaScript 1sleepbysleep / variable_selection
Variable selection for NIR spectral analysis(regression and classification) based on WRC, VIP, SFS, and SPA
Language:Python 1AlexSpradling / Home-Price-Prediction-in-Ames-IA
Cracking the famous Ames, IA dataset with multiple linear regression.
Language:Jupyter Notebook 0drnitinmalik / multiple-linear-regression
Predicting net yearly revenue of Top 50 US startups on the basis of their financial data.
Language:Python 0embarbos / air-pollution-data-analysis
Final Project for STA 135 with Dr. Xiucai Ding
0IshitaBharadwaj / Derby-Horse-Racing-DA
Big Data Derby Racing Dataset's Analysis Project
Language:Jupyter Notebook 0matthewfishermv / MachineLearning-with-R
Machine Learning algorithms in R
Language:R 0meaganng / 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.
Language:R 0PatilSukanya / Assignment-05.-Multiple-Linear-regression-Q2
Used libraries and functions as follows:
Language:Jupyter Notebook 0- Language:Python 0
tissyamalik / mlr-boston-house-pricing
Predicting House Price on Boston dataset using Multiple Linear Regression model
Language:Jupyter Notebook 0yuliyamkh / sleep-disorder-prediction
Sleep Disorder Prediction with Multinomial Logistic Regression
Language:Python 0Bonniface / Study-Materials
Where I keep my Codes for the New thing I learn.
Language:Jupyter NotebookHimnish / 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.
Language:R
