class: title-slide, center, bottom # tidymodels ## R-Ladies East Lansing February ### Julia Silge --- name: clouds2 background-image: url(images/Clouds2.jpg) background-size: cover --- template: clouds2 class: middle, center # <i class="fas fa-heart"></i> Many thanks to Alison Hill and Desirée De Leon for their contributions to this talk --- name: clouds class: center, middle background-image: url(images/Clouds.jpg) background-size: cover --- template: clouds ## .big-text[Hello] --- template: clouds class: middle, center ### Julia Silge <img style="border-radius: 50%;" src="https://github.com/juliasilge.png" width="150px"/> [<svg viewBox="0 0 496 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg"> <path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"></path></svg> @juliasilge](https://github.com/juliasilge) [<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg"> <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 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155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"></path></svg> juliasilge.com](https://juliasilge.com) --- class: inverse, middle, center # Machine learning with tidymodels --- class: top, center background-image: url(images/intro.002.jpeg) background-size: cover --- class: top, center background-image: url(images/intro.003.jpeg) background-size: cover --- class: top, center background-image: url(images/all-of-ml.jpg) background-size: contain .footnote[Credit: <https://vas3k.com/blog/machine_learning/>] --- background-image: url(images/tm-org.png) background-size: contain --- ```r library(tidymodels) ## ── Attaching packages ────────────────────────── tidymodels 0.1.4 ── ## ✓ broom 0.7.12 ✓ rsample 0.1.1 ## ✓ dials 0.1.0 ✓ tune 0.1.6 ## ✓ infer 1.0.0 ✓ workflows 0.2.4 ## ✓ modeldata 0.1.1 ✓ workflowsets 0.1.0 ## ✓ parsnip 0.1.7 ✓ yardstick 0.0.9 ## ✓ recipes 0.1.17 ## ── Conflicts ───────────────────────────── tidymodels_conflicts() ── ## x scales::discard() masks purrr::discard() ## x dplyr::filter() masks stats::filter() ## x recipes::fixed() masks stringr::fixed() ## x dplyr::lag() masks stats::lag() ## x yardstick::spec() masks readr::spec() ## x recipes::step() masks stats::step() ## • Search for functions across packages at https://www.tidymodels.org/find/ ``` --- class: middle, center, frame # Three topics for today What makes a model? Spend your data budget wisely Feature engineering --- class: title-slide, center, bottom # What makes a model? ## tidymodels --- name: train-love background-image: url(images/train.jpg) background-size: contain background-color: #f6f6f6 class: bottom Modeling in R has heterogeneous practices around model interfaces, fitting, and execution. --- class: middle, center, frame # parsnip <iframe src="https://parsnip.tidymodels.org" width="100%" height="400px" data-external="1"></iframe> --- class: middle, frame # .center[To specify a model in tidymodels] .right-column[ 1\. Pick a .display[model] 2\. Set the .display[mode] (if needed) 3\. Set the .display[engine] ] --- class: middle, frame .fade[ # .center[To specify a model in tidymodels] ] .right-column[ 1\. Pick a .display[model] .fade[ 2\. Set the .display[mode] (if needed) 3\. Set the .display[engine] ] ] --- class: middle, center, frame # 1\. Pick a .display[model] All available models are listed at <https://tidymodels.org/find/parsnip> <iframe src="https://tidymodels.org/find/parsnip" width="100%" height="400px" data-external="1"></iframe> --- class: middle .center[ # `linear_reg()` Specifies a linear regression model ] ```r linear_reg(penalty = NULL, mixture = NULL) ``` --- class: middle .center[ # `decision_tree()` Specifies a decision tree model ] ```r decision_tree(cost_complexity = NULL, tree_depth = NULL, min_n = NULL) ``` --- class: middle .center[ # `rand_forest()` Specifies a random forest model ] ```r rand_forest(mtry = NULL, trees = NULL, min_n = NULL) ``` --- class: middle, frame .fade[ # .center[To specify a model in tidymodels] ] .right-column[ .fade[ 1\. Pick a .display[model] ] 2\. Set the .display[mode] (if needed) .fade[ 3\. Set the .display[engine] ] ] --- class: middle # `set_mode()` Some models can solve multiple types of problems ```r linear_reg() %>% set_mode(mode = "regression") ## Linear Regression Model Specification (regression) ## ## Computational engine: lm ``` --- class: middle # `set_mode()` Some models can solve multiple types of problems ```r logistic_reg() %>% set_mode(mode = "classification") ## Logistic Regression Model Specification (classification) ## ## Computational engine: glm ``` --- class: middle # `set_mode()` Some models can solve multiple types of problems ```r decision_tree() %>% set_mode(mode = "classification") ## Decision Tree Model Specification (classification) ## ## Computational engine: rpart ``` --- class: middle # `set_mode()` Some models can solve multiple types of problems ```r decision_tree() %>% set_mode(mode = "regression") ## Decision Tree Model Specification (regression) ## ## Computational engine: rpart ``` --- class: middle, frame .fade[ # .center[To specify a model in tidymodels] ] .right-column[ .fade[ 1\. Pick a .display[model] 2\. Set the .display[mode] (if needed) ] 3\. Set the .display[engine] ] --- class: middle # `set_engine()` The same model type can be implemented by multiple computational engines ```r rand_forest() %>% set_engine("randomForest") ## Random Forest Model Specification (unknown) ## ## Computational engine: randomForest ``` --- class: middle # `set_engine()` The same model type can be implemented by multiple computational engines ```r rand_forest() %>% set_engine("ranger") ## Random Forest Model Specification (unknown) ## ## Computational engine: ranger ``` --- class: middle # `set_engine()` The same model type can be implemented by multiple computational engines ```r linear_reg() %>% set_engine("lm") ## Linear Regression Model Specification (regression) ## ## Computational engine: lm ``` --- class: middle # `set_engine()` The same model type can be implemented by multiple computational engines ```r linear_reg() %>% set_engine("spark") ## Linear Regression Model Specification (regression) ## ## Computational engine: spark ``` --- class: middle, frame # .center[What makes a model?] ```r nearest_neighbor() %>% set_engine("kknn") %>% set_mode("regression") ## K-Nearest Neighbor Model Specification (regression) ## ## Computational engine: kknn ``` --- class: middle, frame # .center[Harmonize heterogeneous interfaces] |**parsnip** |**xgboost** |**C5.0** |**spark** | |:--------------|:--------------------|:------------|:-----------------------------------| |tree_depth |max_depth (6) |NA |max_depth (5) | |trees |nrounds (15) |trials (15) |max_iter (20) | |learn_rate |eta (0.3) |NA |step_size (0.1) | |mtry |colsample_bytree (1) |NA |feature_subset_strategy (1 or 5) | |min_n |min_child_weight (1) |minCases (2) |min_instances_per_node (1) | |loss_reduction |gamma (0) |NA |min_info_gain (0) | |sample_size |subsample (1) |sample (0) |subsampling_rate (1) | |stop_iter |early_stop |NA |NA | --- class: title-slide, center, bottom # Spending your data budget ## tidymodels --- class: middle, center, frame # rsample <iframe src="https://rsample.tidymodels.org" width="100%" height="400px" data-external="1"></iframe> --- class: middle, center, frame # Data splitting <img src="index_files/figure-html/all-split-1.png" width="864" /> --- # `initial_split()` Splits data randomly into a single testing and a single training set ```r ames_split <- initial_split(ames, prop = 0.75) ames_split ## <Analysis/Assess/Total> ## <2197/733/2930> ``` --- # `training()` and `testing()` Create training and testing sets from an `rsplit` ```r ames_train <- training(ames_split) ames_train ## # A tibble: 2,197 × 81 ## MS_SubClass MS_Zoning Lot_Frontage Lot_Area Street Alley ## <fct> <fct> <dbl> <int> <fct> <fct> ## 1 One_Story_19… Resident… 80 11600 Pave No_A… ## 2 One_Story_19… Resident… 0 9500 Pave No_A… ## 3 One_and_Half… Resident… 60 8400 Pave No_A… ## 4 One_Story_19… Resident… 75 9000 Pave No_A… ## 5 Two_Story_19… Resident… 80 10791 Pave No_A… ## # … with 2,192 more rows, and 75 more variables: ## # Lot_Shape <fct>, Land_Contour <fct>, Utilities <fct>, ## # Lot_Config <fct>, Land_Slope <fct>, … ``` --- # `training()` and `testing()` Create training and testing sets from an `rsplit` ```r ames_test <- testing(ames_split) ames_test ## # A tibble: 733 × 81 ## MS_SubClass MS_Zoning Lot_Frontage Lot_Area Street Alley ## <fct> <fct> <dbl> <int> <fct> <fct> ## 1 One_Story_19… Resident… 80 11622 Pave No_A… ## 2 One_Story_19… Resident… 81 14267 Pave No_A… ## 3 Two_Story_19… Resident… 74 13830 Pave No_A… ## 4 One_Story_PU… Resident… 41 4920 Pave No_A… ## 5 One_Story_PU… Resident… 43 5005 Pave No_A… ## # … with 728 more rows, and 75 more variables: ## # Lot_Shape <fct>, Land_Contour <fct>, Utilities <fct>, ## # Lot_Config <fct>, Land_Slope <fct>, … ``` --- background-image: url(images/diamonds.jpg) background-size: contain background-position: left class: middle, center background-color: #f5f5f5 .pull-right[ ## The .display[testing set] is precious ## We can only use it once! ] --- template: clouds ## How can we use the training set to compare, evaluate, and tune models? --- background-image: url(https://www.tidymodels.org/start/resampling/img/resampling.svg) background-size: 60% --- ```r set.seed(123) vfold_cv(ames_train, strata = Sale_Price) ## # 10-fold cross-validation using stratification ## # A tibble: 10 × 2 ## splits id ## <list> <chr> ## 1 <split [1975/222]> Fold01 ## 2 <split [1976/221]> Fold02 ## 3 <split [1976/221]> Fold03 ## 4 <split [1977/220]> Fold04 ## 5 <split [1977/220]> Fold05 ## 6 <split [1978/219]> Fold06 ## 7 <split [1978/219]> Fold07 ## 8 <split [1978/219]> Fold08 ## 9 <split [1979/218]> Fold09 ## 10 <split [1979/218]> Fold10 ``` --- class: middle, center, inverse # Cross-validation --- background-image: url(images/cross-validation/Slide2.png) background-size: contain --- background-image: url(images/cross-validation/Slide3.png) background-size: contain --- background-image: url(images/cross-validation/Slide4.png) background-size: contain --- background-image: url(images/cross-validation/Slide5.png) background-size: contain --- background-image: url(images/cross-validation/Slide6.png) background-size: contain --- background-image: url(images/cross-validation/Slide7.png) background-size: contain --- background-image: url(images/cross-validation/Slide8.png) background-size: contain --- background-image: url(images/cross-validation/Slide9.png) background-size: contain --- background-image: url(images/cross-validation/Slide10.png) background-size: contain --- background-image: url(images/cross-validation/Slide11.png) background-size: contain --- ```r set.seed(123) vfold_cv(ames_train, strata = Sale_Price) ## # 10-fold cross-validation using stratification ## # A tibble: 10 × 2 ## splits id ## <list> <chr> ## 1 <split [1975/222]> Fold01 ## 2 <split [1976/221]> Fold02 ## 3 <split [1976/221]> Fold03 ## 4 <split [1977/220]> Fold04 ## 5 <split [1977/220]> Fold05 ## 6 <split [1978/219]> Fold06 ## 7 <split [1978/219]> Fold07 ## 8 <split [1978/219]> Fold08 ## 9 <split [1979/218]> Fold09 ## 10 <split [1979/218]> Fold10 ``` --- class: middle, center .center[ # Resampling methods .display[Spend your data wisely] to create simulated validation set ] ```r vfold_cv() loo_cv() mc_cv() bootstraps() ``` --- class: title-slide, center, bottom # Feature engineering ## tidymodels --- background-image: url(images/two-birds2-alpha.png) background-size: contain background-position: left class: middle, center background-color: #f5f5f5 .pull-right[ # Let's go back to the beginning ] --- class: middle, center, frame # recipes <iframe src="https://recipes.tidymodels.org" width="100%" height="400px" data-external="1"></iframe> --- background-image: url(images/garbage.jpg) background-size: contain background-position: right class: middle, center background-color: #f5f5f5 .pull-left[ # Build better predictors ] --- class: middle, center, frame # To build a recipe 1\. Start the `recipe()` 2\. Define the .display[variables] involved 3\. Describe preprocessing .display[step-by-step] --- class: middle, center # `recipe()` Creates a recipe for a set of variables ```r recipe(Sale_Price ~ ., data = ames) ``` --- class: middle, center # .center[`step_*()`] Complete list at <https://recipes.tidymodels.org/reference/> <iframe src="https://recipes.tidymodels.org/reference/" width="100%" height="400px" data-external="1"></iframe> --- background-image: url(images/cranes.jpg) background-position: left background-size: contain class: middle .right-column[ # Preprocessing options + Encode categorical predictors + Center and scale variables + Handle class imbalance + Impute missing data + Perform dimensionality reduction + *A lot more!* ] --- Estimate parameters for preprocessing using the .display[training data] ```r pca_rec <- recipe(Sale_Price ~ ., data = ames_train) %>% step_novel(all_nominal()) %>% step_dummy(all_nominal()) %>% step_zv(all_predictors()) %>% step_normalize(all_predictors()) %>% step_pca(all_predictors(), num_comp = 5) ``` --- ```r prep(pca_rec) ## Recipe ## ## Inputs: ## ## role #variables ## outcome 1 ## predictor 80 ## ## Training data contained 2197 data points and no missing data. ## ## Operations: ## ## Novel factor level assignment for MS_SubClass, MS_Zoning, Street, Alley, Lot_Shape, Land_Contour, Utilities,... [trained] ## Dummy variables from MS_SubClass, MS_Zoning, Street, Alley, Lot_Shape, Land_Contour, Utilities, Lot_Confi... [trained] ## Zero variance filter removed MS_SubClass_new, MS_Zoning_new, Street_new, Alley_new, Lot_Shape_n... [trained] ## Centering and scaling for Lot_Frontage, Lot_Area, Year_Built, Year_Remod_Add, Mas_Vnr_Area, BsmtFin_... [trained] ## PCA extraction with Lot_Frontage, Lot_Area, Year_Built, Year_Remod_Add, Mas_Vnr_Area, BsmtFin_S... [trained] ``` --- background-image: url(images/workflows/workflows.008.jpeg) background-size: contain --- template: clouds ## .big-text[More to learn!] --- template: clouds2 class: middle, right ## Thanks! [<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg"> <path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"></path></svg> tidymodels.org](https://tidymodels.org) [<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg"> <path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 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