how to install r studio for machine learning

#HOW TO INSTALL R STUDIO FOR MACHINE LEARNING HOW TO#
#HOW TO INSTALL R STUDIO FOR MACHINE LEARNING SOFTWARE#
#HOW TO INSTALL R STUDIO FOR MACHINE LEARNING DOWNLOAD#

Highly Recommended: Watch this video made by Professor Lisa Lendway that describes essential configuration options for RStudio.

As of August 19, 2021, the latest version of RStudio is.

Click the button under step 2 to install the version of RStudio recommended for your computer.

As of August 19, 2021, the latest version of R is 4.1.1.

Choose the link that corresponds to your computer.

You will see three links “Download R for …”.

#HOW TO INSTALL R STUDIO FOR MACHINE LEARNING HOW TO#

This page has information on how to do this for the most common browsers.

It is highly recommended that you change this option so that your browser asks you where to save each file before downloading it. This does not encourage good file organization practices.

Generally by default, internet browsers automatically save all files to the Downloads folder on your computer.

#HOW TO INSTALL R STUDIO FOR MACHINE LEARNING DOWNLOAD#

Highly recommended: Change the default file download location for your internet browser. Even if you’ve already downloaded both R and RStudio, you’ll want to re-download to make sure that you have the most current versions.

#HOW TO INSTALL R STUDIO FOR MACHINE LEARNING SOFTWARE#

Open Communication about Expectations and Goalsīefore the first day of class, you should follow these instructions to set up the software that we’ll be using throughout the semester.

Exercise 4: Scree plots and dimension reduction.

Exercise 4: Tree-cutting and interpretation.

Exercise 3: Interpreting the clusters visually.

Exercise 2: Exploring penguin dendrograms.

Exercise 1: Hierarchical clustering by hand.

Exercise 4: Clustering on more variables.

Exercise 2: K-means clustering on bill length and depth.

Exercise 6: Variable importance measures.

Exercise 2: Preparation to build a random forest.

Exercise 1: Bagging: Bootstrap Aggregation.

Exercise 6: Variable importance in trees.

Exercise 3: Building & tuning trees in tidymodels.

Exercise 2: Core theme: Tuning parameters and the BVT.

Exercise 1: Core theme: parametric/nonparametric.

Exercise 6: Algorithmic understanding for evaluation metrics.

Exercise 5: Using the final model (choosing a threshold).

Exercise 4: Interpreting evaluation metrics.

Exercise 2: Implementing LASSO logistic regression in tidymodels.

LASSO for logistic regression in tidymodels.

Exercise 2: Implementing logistic regression in tidymodels.

Exercise 3: Building a GAM in tidymodels.

Exercise 1: Evaluating a fully linear model.

Exercise 3: Implementing KNN in tidymodels.

Exercise 2: Impact of variable scale and distance measure.

Exercise 1: Bias-variance tradeoff warmup.

7 KNN Regression and the Bias-Variance Tradeoff.

Exercise 4: Examining and evaluating the best LASSO model.

Exercise 3: Examining output: plot of coefficient paths.

Exercise 2: Fitting a LASSO model in tidymodels.

Exercise 4: Approaches to variable subset selection.

Exercise 3: Planning forward selection using CV.

Exercise 1: Backward stepwise selection: by hand.

Exercise 4: Practical issues: choosing the number of folds.

Exercise 3: Looking at the evaluation metrics.

Exercise 2: Cross-validation with tidymodels.

Exercise 1: Cross-validation in Concept.

How to install r studio for machine learning