stephlocke

library(rtweet) #rtweet API creds should already be set up
library(stringi)
library(dplyr)

friends = get_friends(user="stephlocke")
followers = get_followers("stephlocke")
tweeps_id = distinct(bind_rows(friends, followers))
tweeps_info = lookup_users(tweeps_id$user_id)

# A regex for a visit to Durham

stephlocke

This post provides an overview of performing diagnostic and performance evaluation on logistic regression models in R. After training a statistical model, it’s important to understand how well that model did in regards to it’s accuracy and predictive power. The following content will provide the background and theory to ensure that the right technique are being utilized for evaluating logistic regression models in R.

Logistic Regression Example
We will use the GermanCredit dataset in the caret package for this example. It contains 62 characteristics and 1000 observations, with a target variable (Class) that is allready defined. The response variable is coded 0 for bad consumer and 1 for good. It’s always recommended that one looks at the coding of the response variable to ensure that it’s a factor variable that’s coded accurately with a 0/1 scheme or two factor levels in the right order. The first step is to partition the data into training and testing sets.

```
library(caret)
data(GermanCredit)
Train <- cr

stephlocke

Issue	pkg..project	repo	group
	mwparser	https://github.com/ropenscilabs/mwparser	Oliver
5	checkers	https://github.com/ropenscilabs/checkers	Noam, Nick, Alice, Laura, Nistara, Scott, Molly
13	miner	https://github.com/ROpenSciLabs/miner	Karl, David, Gergely, Brooke, Ali
28	emldown	https://github.com/ro