R Packages for Regression For this post we will present some valuable R packages for using in regression studies. Check it out! stats Package very useful for statistical calculations and random number generations. Below you can find the most useful function in regression area: lm(): it is used to fit linear models summary.lm(): thsi function returns a summary for linear model fits coef(): it is possible obtain the coefficients from modeling functions
Random Forest In this post we will explore some ideas around the Random Forest model Objective We are working on in the dataset called Boston Housing and the main idea here is regression task and we are concerned with modeling the price of houses in thousands of dollars in the Surburb of Boston. So, we are dirting our hands in a regression predictive modeling problem. The main goal here is to fit a regression model that best explains the variation in medv variable.
R Markdown This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com. You can embed an R code chunk like this: summary(cars) ## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.00 ## Mean :15.4 Mean : 42.98 ## 3rd Qu.
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