Machine learning

Introduction to Unsupervised Learning

Unsupervised Learning Supervised learning has the goal of making predictions with a set of known labels for the response variable. In unsupervised learning, we try to find structure in the data of the response variable without predetermined labels. Goal: organize the items available in the Animal Crossing video game Data set: Animal Crossing Source: VillagerDB, MetaCritic, and TidyTuesday Animal Crossing Tidy Tuesday library("ggrepel") library("tidyverse") # critic <- readr::read_tsv('https://raw.

Introduction to Unsupervised Learning

Unsupervised Learning Supervised learning has the goal of making predictions with a set of known labels for the response variable. In unsupervised learning, we try to find structure in the data of the response variable without predetermined labels. Goal: organize the items available in the Animal Crossing video game Data set: Animal Crossing Source: VillagerDB, MetaCritic, and TidyTuesday Animal Crossing Tidy Tuesday library("ggrepel") library("tidyverse") # critic <- readr::read_tsv(‘https://raw.

Supervised Learning

Supervised Learning Supervised learning has the goal of making predictions with a set of known labels for the response variable. In unsupervised learning, we try to find structure in the data of the response variable without predetermined labels. Goal: predict the personality type of each character in Animal Crossing Data set: Animal Crossing Source: VillagerDB, MetaCritic, and TidyTuesday Animal Crossing Tidy Tuesday library("caret") library("randomForest") library("tidymodels") library("tidyverse") # critic <- readr::read_tsv('https://raw.

Introduction to Machine Learning

Goals for today introduce machine learning (ideas and terminology) introduce tidymodels package practice with a TidyTuesday data set library("tidymodels") library("tidyverse") Data: Tour de France Source: TidyTuesday data set from April 7, 2020 tdf_winners <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-04-07/tdf_winners.csv&#39;) str(tdf_winners) ## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 106 obs. of 19 variables: ## $ edition : num 1 2 3 4 5 6 7 8 9 10 … ## $ start_date : Date, format: "1903-07-01" "1904-07-02" .