So, which is the best of these 5 packages ? I am sure many of you would be asking this! Having created this tutorial, I felt Hmisc should be your first choice of missing value imputation followed by missForest and MICE.
Hmisc automatically recognizes the variables types and uses bootstrap sample and predictive mean matching to impute missing values. You don’t need to separate or treat categorical variable, just like we did while using MICE package. However, missForest can outperform Hmisc if the observed variables supplied contain sufficient information.
path setwd(path)\n\n#load data\n> data summary(iris)\n\n#Generate 10% missing values at Random \n> iris.mis summary(iris.mis)\n","classes":{"has":1}}" data-cke-widget-upcasted="1" data-cke-widget-keep-attr="0" data-widget="codeSnippet">> path setwd(path) #load data > data summary(iris) #Generate 10% missing values at Random > iris.mis summary(iris.mis)
ERROR: tensorflow 2.1.0 has requirement scipy==1.4.1; python_version >= “3”, but you’ll have scipy 1.1.0 which is incompatible.
ERROR: tensorflow 2.1.0 has requirement six>=1.12.0, but you’ll have six 1.11.0 which is incompatible.
ERROR: Cannot uninstall ‘wrapt’. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
【4】不要怕,继续往下安装
pip install --upgrade scipy==1.4.1
pip install --upgrade six==1.12.0
pip install wrapt --ignore-installed
【5】哇塞,咋还有可能出错
ImportError: Could not find the DLL(s) ‘msvcp140_1.dll’