From 558f25f80d47c7ed456f981c720a283261be314f Mon Sep 17 00:00:00 2001 From: R-icntay <63848664+R-icntay@users.noreply.github.com> Date: Mon, 23 Aug 2021 18:40:25 +0300 Subject: [PATCH] Update and add R Notebooks to the R folder --- .../2-Classifiers-1/solution/R/README.md | 1 - .../solution/{ => R}/lesson_11-R.ipynb | 844 +++++++++++------- .../solution/{ => R}/lesson_11.Rmd | 4 +- 3 files changed, 516 insertions(+), 333 deletions(-) delete mode 100644 4-Classification/2-Classifiers-1/solution/R/README.md rename 4-Classification/2-Classifiers-1/solution/{ => R}/lesson_11-R.ipynb (59%) rename 4-Classification/2-Classifiers-1/solution/{ => R}/lesson_11.Rmd (99%) diff --git a/4-Classification/2-Classifiers-1/solution/R/README.md b/4-Classification/2-Classifiers-1/solution/R/README.md deleted file mode 100644 index f59c07cc..00000000 --- a/4-Classification/2-Classifiers-1/solution/R/README.md +++ /dev/null @@ -1 +0,0 @@ -this is a temporary placeholder \ No newline at end of file diff --git a/4-Classification/2-Classifiers-1/solution/lesson_11-R.ipynb b/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb similarity index 59% rename from 4-Classification/2-Classifiers-1/solution/lesson_11-R.ipynb rename to 4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb index 30fb66c7..bd1aa914 100644 --- a/4-Classification/2-Classifiers-1/solution/lesson_11-R.ipynb +++ b/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb @@ -1,6 +1,6 @@ { "nbformat": 4, - "nbformat_minor": 0, + "nbformat_minor": 2, "metadata": { "colab": { "name": "lesson_11-R.ipynb", @@ -19,18 +19,15 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "id": "zs2woWv_HoE8" - }, "source": [ "# Build a classification model: Delicious Asian and Indian Cuisines" - ] + ], + "metadata": { + "id": "zs2woWv_HoE8" + } }, { "cell_type": "markdown", - "metadata": { - "id": "iDFOb3ebHwQC" - }, "source": [ "## Cuisine classifiers 1\n", "\n", @@ -66,51 +63,51 @@ "- `nnet`: The [nnet package](https://cran.r-project.org/web/packages/nnet/nnet.pdf) provides functions for estimating feed-forward neural networks with a single hidden layer, and for multinomial logistic regression models.\n", "\n", "You can have them installed as:" - ] + ], + "metadata": { + "id": "iDFOb3ebHwQC" + } }, { "cell_type": "markdown", - "metadata": { - "id": "4V85BGCjII7F" - }, "source": [ "\n", "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n", "\n", "Alternatively, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing." - ] + ], + "metadata": { + "id": "4V85BGCjII7F" + } }, { "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "an5NPyyKIKNR", - "outputId": "834d5e74-f4b8-49f9-8ab5-4c52ff2d7bc8" - }, + "execution_count": 2, "source": [ - "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n", - "\n", + "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n", + "\r\n", "pacman::p_load(tidyverse, tidymodels, themis, here)" ], - "execution_count": 2, "outputs": [ { "output_type": "stream", + "name": "stderr", "text": [ "Loading required package: pacman\n", "\n" - ], - "name": "stderr" + ] } - ] + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "an5NPyyKIKNR", + "outputId": "834d5e74-f4b8-49f9-8ab5-4c52ff2d7bc8" + } }, { "cell_type": "markdown", - "metadata": { - "id": "0ax9GQLBINVv" - }, "source": [ "Now, let's hit the ground running!\n", "\n", @@ -121,41 +118,37 @@ "### Drop the most common ingredients that create confusion between distinct cuisines, using `dplyr::select()`.\n", "\n", "Everyone loves rice, garlic and ginger!\n" - ] + ], + "metadata": { + "id": "0ax9GQLBINVv" + } }, { "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 735 - }, - "id": "jhCrrH22IWVR", - "outputId": "d444a85c-1d8b-485f-bc4f-8be2e8f8217c" - }, + "execution_count": 3, "source": [ - "# Load the original cuisines data\n", - "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\n", - "\n", - "# Drop id column, rice, garlic and ginger from our original data set\n", - "df_select <- df %>% \n", - " select(-c(1, rice, garlic, ginger)) %>%\n", - " # Encode cuisine column as categorical\n", - " mutate(cuisine = factor(cuisine))\n", - "\n", - "# Display new data set\n", - "df_select %>% \n", - " slice_head(n = 5)\n", - "\n", - "# Display distribution of cuisines\n", - "df_select %>% \n", - " count(cuisine) %>% \n", + "# Load the original cuisines data\r\n", + "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n", + "\r\n", + "# Drop id column, rice, garlic and ginger from our original data set\r\n", + "df_select <- df %>% \r\n", + " select(-c(1, rice, garlic, ginger)) %>%\r\n", + " # Encode cuisine column as categorical\r\n", + " mutate(cuisine = factor(cuisine))\r\n", + "\r\n", + "# Display new data set\r\n", + "df_select %>% \r\n", + " slice_head(n = 5)\r\n", + "\r\n", + "# Display distribution of cuisines\r\n", + "df_select %>% \r\n", + " count(cuisine) %>% \r\n", " arrange(desc(n))" ], - "execution_count": 3, "outputs": [ { "output_type": "stream", + "name": "stderr", "text": [ "New names:\n", "* `` -> ...1\n", @@ -171,8 +164,7 @@ "\u001b[36mℹ\u001b[39m Use \u001b[30m\u001b[47m\u001b[30m\u001b[47m`spec()`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to retrieve the full column specification for this data.\n", "\u001b[36mℹ\u001b[39m Specify the column types or set \u001b[30m\u001b[47m\u001b[30m\u001b[47m`show_col_types = FALSE`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to quiet this message.\n", "\n" - ], - "name": "stderr" + ] }, { "output_type": "display_data", @@ -197,8 +189,32 @@ "4 0 0 0 0 \n", "5 0 0 1 0 " ], - "text/latex": "A tibble: 5 × 381\n\\begin{tabular}{lllllllllllllllllllll}\n cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n\\hline\n\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n\t indian & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\\n\\end{tabular}\n", - "text/markdown": "\nA tibble: 5 × 381\n\n| cuisine <fct> | almond <dbl> | angelica <dbl> | anise <dbl> | anise_seed <dbl> | apple <dbl> | apple_brandy <dbl> | apricot <dbl> | armagnac <dbl> | artemisia <dbl> | ⋯ ⋯ | whiskey <dbl> | white_bread <dbl> | white_wine <dbl> | whole_grain_wheat_flour <dbl> | wine <dbl> | wood <dbl> | yam <dbl> | yeast <dbl> | yogurt <dbl> | zucchini <dbl> |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| indian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |\n\n", + "text/markdown": [ + "\n", + "A tibble: 5 × 381\n", + "\n", + "| cuisine <fct> | almond <dbl> | angelica <dbl> | anise <dbl> | anise_seed <dbl> | apple <dbl> | apple_brandy <dbl> | apricot <dbl> | armagnac <dbl> | artemisia <dbl> | ⋯ ⋯ | whiskey <dbl> | white_bread <dbl> | white_wine <dbl> | whole_grain_wheat_flour <dbl> | wine <dbl> | wood <dbl> | yam <dbl> | yeast <dbl> | yogurt <dbl> | zucchini <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| indian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |\n", + "\n" + ], + "text/latex": [ + "A tibble: 5 × 381\n", + "\\begin{tabular}{lllllllllllllllllllll}\n", + " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n", + " & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", + "\t indian & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", + "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", + "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", + "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\\n", + "\\end{tabular}\n" + ], "text/html": [ "\n", "\n", @@ -229,8 +245,32 @@ "4 japanese 320\n", "5 thai 289" ], - "text/latex": "A tibble: 5 × 2\n\\begin{tabular}{ll}\n cuisine & n\\\\\n & \\\\\n\\hline\n\t korean & 799\\\\\n\t indian & 598\\\\\n\t chinese & 442\\\\\n\t japanese & 320\\\\\n\t thai & 289\\\\\n\\end{tabular}\n", - "text/markdown": "\nA tibble: 5 × 2\n\n| cuisine <fct> | n <int> |\n|---|---|\n| korean | 799 |\n| indian | 598 |\n| chinese | 442 |\n| japanese | 320 |\n| thai | 289 |\n\n", + "text/markdown": [ + "\n", + "A tibble: 5 × 2\n", + "\n", + "| cuisine <fct> | n <int> |\n", + "|---|---|\n", + "| korean | 799 |\n", + "| indian | 598 |\n", + "| chinese | 442 |\n", + "| japanese | 320 |\n", + "| thai | 289 |\n", + "\n" + ], + "text/latex": [ + "A tibble: 5 × 2\n", + "\\begin{tabular}{ll}\n", + " cuisine & n\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t korean & 799\\\\\n", + "\t indian & 598\\\\\n", + "\t chinese & 442\\\\\n", + "\t japanese & 320\\\\\n", + "\t thai & 289\\\\\n", + "\\end{tabular}\n" + ], "text/html": [ "
A tibble: 5 × 381
\n", "\n", @@ -250,66 +290,66 @@ }, "metadata": {} } - ] + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 735 + }, + "id": "jhCrrH22IWVR", + "outputId": "d444a85c-1d8b-485f-bc4f-8be2e8f8217c" + } }, { "cell_type": "markdown", - "metadata": { - "id": "AYTjVyajIdny" - }, "source": [ "Perfect! Now, time to split the data such that 70% of the data goes to training and 30% goes to testing. We'll also apply a `stratification` technique when splitting the data to `maintain the proportion of each cuisine` in the training and validation datasets.\n", "\n", "[rsample](https://rsample.tidymodels.org/), a package in Tidymodels, provides infrastructure for efficient data splitting and resampling:" - ] + ], + "metadata": { + "id": "AYTjVyajIdny" + } }, { "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 535 - }, - "id": "w5FWIkEiIjdN", - "outputId": "2e195fd9-1a8f-4b91-9573-cce5582242df" - }, + "execution_count": 4, "source": [ - "# Load the core Tidymodels packages into R session\n", - "library(tidymodels)\n", - "\n", - "# Create split specification\n", - "set.seed(2056)\n", - "cuisines_split <- initial_split(data = df_select,\n", - " strata = cuisine,\n", - " prop = 0.7)\n", - "\n", - "# Extract the data in each split\n", - "cuisines_train <- training(cuisines_split)\n", - "cuisines_test <- testing(cuisines_split)\n", - "\n", - "# Print the number of cases in each split\n", - "cat(\"Training cases: \", nrow(cuisines_train), \"\\n\",\n", - " \"Test cases: \", nrow(cuisines_test), sep = \"\")\n", - "\n", - "# Display the first few rows of the training set\n", - "cuisines_train %>% \n", - " slice_head(n = 5)\n", - "\n", - "\n", - "# Display distribution of cuisines in the training set\n", - "cuisines_train %>% \n", - " count(cuisine) %>% \n", + "# Load the core Tidymodels packages into R session\r\n", + "library(tidymodels)\r\n", + "\r\n", + "# Create split specification\r\n", + "set.seed(2056)\r\n", + "cuisines_split <- initial_split(data = df_select,\r\n", + " strata = cuisine,\r\n", + " prop = 0.7)\r\n", + "\r\n", + "# Extract the data in each split\r\n", + "cuisines_train <- training(cuisines_split)\r\n", + "cuisines_test <- testing(cuisines_split)\r\n", + "\r\n", + "# Print the number of cases in each split\r\n", + "cat(\"Training cases: \", nrow(cuisines_train), \"\\n\",\r\n", + " \"Test cases: \", nrow(cuisines_test), sep = \"\")\r\n", + "\r\n", + "# Display the first few rows of the training set\r\n", + "cuisines_train %>% \r\n", + " slice_head(n = 5)\r\n", + "\r\n", + "\r\n", + "# Display distribution of cuisines in the training set\r\n", + "cuisines_train %>% \r\n", + " count(cuisine) %>% \r\n", " arrange(desc(n))" ], - "execution_count": 4, "outputs": [ { "output_type": "stream", + "name": "stdout", "text": [ "Training cases: 1712\n", "Test cases: 736" - ], - "name": "stdout" + ] }, { "output_type": "display_data", @@ -334,8 +374,32 @@ "4 0 0 0 0 \n", "5 0 0 0 0 " ], - "text/latex": "A tibble: 5 × 381\n\\begin{tabular}{lllllllllllllllllllll}\n cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n\\hline\n\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n\\end{tabular}\n", - "text/markdown": "\nA tibble: 5 × 381\n\n| cuisine <fct> | almond <dbl> | angelica <dbl> | anise <dbl> | anise_seed <dbl> | apple <dbl> | apple_brandy <dbl> | apricot <dbl> | armagnac <dbl> | artemisia <dbl> | ⋯ ⋯ | whiskey <dbl> | white_bread <dbl> | white_wine <dbl> | whole_grain_wheat_flour <dbl> | wine <dbl> | wood <dbl> | yam <dbl> | yeast <dbl> | yogurt <dbl> | zucchini <dbl> |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n\n", + "text/markdown": [ + "\n", + "A tibble: 5 × 381\n", + "\n", + "| cuisine <fct> | almond <dbl> | angelica <dbl> | anise <dbl> | anise_seed <dbl> | apple <dbl> | apple_brandy <dbl> | apricot <dbl> | armagnac <dbl> | artemisia <dbl> | ⋯ ⋯ | whiskey <dbl> | white_bread <dbl> | white_wine <dbl> | whole_grain_wheat_flour <dbl> | wine <dbl> | wood <dbl> | yam <dbl> | yeast <dbl> | yogurt <dbl> | zucchini <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n", + "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n", + "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", + "\n" + ], + "text/latex": [ + "A tibble: 5 × 381\n", + "\\begin{tabular}{lllllllllllllllllllll}\n", + " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n", + " & & & & & & & & & & ⋯ & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n", + "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n", + "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", + "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", + "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", + "\\end{tabular}\n" + ], "text/html": [ "
A tibble: 5 × 2
\n", "\n", @@ -366,8 +430,32 @@ "4 japanese 224\n", "5 thai 202" ], - "text/latex": "A tibble: 5 × 2\n\\begin{tabular}{ll}\n cuisine & n\\\\\n & \\\\\n\\hline\n\t korean & 559\\\\\n\t indian & 418\\\\\n\t chinese & 309\\\\\n\t japanese & 224\\\\\n\t thai & 202\\\\\n\\end{tabular}\n", - "text/markdown": "\nA tibble: 5 × 2\n\n| cuisine <fct> | n <int> |\n|---|---|\n| korean | 559 |\n| indian | 418 |\n| chinese | 309 |\n| japanese | 224 |\n| thai | 202 |\n\n", + "text/markdown": [ + "\n", + "A tibble: 5 × 2\n", + "\n", + "| cuisine <fct> | n <int> |\n", + "|---|---|\n", + "| korean | 559 |\n", + "| indian | 418 |\n", + "| chinese | 309 |\n", + "| japanese | 224 |\n", + "| thai | 202 |\n", + "\n" + ], + "text/latex": [ + "A tibble: 5 × 2\n", + "\\begin{tabular}{ll}\n", + " cuisine & n\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t korean & 559\\\\\n", + "\t indian & 418\\\\\n", + "\t chinese & 309\\\\\n", + "\t japanese & 224\\\\\n", + "\t thai & 202\\\\\n", + "\\end{tabular}\n" + ], "text/html": [ "
A tibble: 5 × 381
\n", "\n", @@ -387,13 +475,18 @@ }, "metadata": {} } - ] + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 535 + }, + "id": "w5FWIkEiIjdN", + "outputId": "2e195fd9-1a8f-4b91-9573-cce5582242df" + } }, { "cell_type": "markdown", - "metadata": { - "id": "daBi9qJNIwqW" - }, "source": [ "## 2. Deal with imbalanced data\n", "\n", @@ -406,30 +499,25 @@ "- removing observations from majority class: `Under-sampling`\n", "\n", "In our previous lesson, we demonstrated how to deal with imbalanced data sets using a `recipe`. A recipe can be thought of as a blueprint that describes what steps should be applied to a data set in order to get it ready for data analysis. In our case, we want to have an equal distribution in the number of our cuisines for our `training set`. Let's get right into it." - ] + ], + "metadata": { + "id": "daBi9qJNIwqW" + } }, { "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 200 - }, - "id": "Az6LFBGxI1X0", - "outputId": "29d71d85-64b0-4e62-871e-bcd5398573b6" - }, + "execution_count": 5, "source": [ - "# Load themis package for dealing with imbalanced data\n", - "library(themis)\n", - "\n", - "# Create a recipe for preprocessing training data\n", - "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>% \n", - " step_smote(cuisine)\n", - "\n", - "# Print recipe\n", + "# Load themis package for dealing with imbalanced data\r\n", + "library(themis)\r\n", + "\r\n", + "# Create a recipe for preprocessing training data\r\n", + "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>% \r\n", + " step_smote(cuisine)\r\n", + "\r\n", + "# Print recipe\r\n", "cuisines_recipe" ], - "execution_count": 5, "outputs": [ { "output_type": "display_data", @@ -450,88 +538,93 @@ }, "metadata": {} } - ] + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 200 + }, + "id": "Az6LFBGxI1X0", + "outputId": "29d71d85-64b0-4e62-871e-bcd5398573b6" + } }, { "cell_type": "markdown", + "source": [ + "You can of course go ahead and confirm (using prep+bake) that the recipe will work as you expect it - all the cuisine labels having `559` observations.\r\n", + "\r\n", + "Since we'll be using this recipe as a preprocessor for modeling, a `workflow()` will do all the prep and bake for us, so we won't have to manually estimate the recipe.\r\n", + "\r\n", + "Now we are ready to train a model 👩‍💻👨‍💻!\r\n", + "\r\n", + "## 3. Choosing your classifier\r\n", + "\r\n", + "

\r\n", + " \r\n", + "

Artwork by @allison_horst
\r\n" + ], "metadata": { "id": "NBL3PqIWJBBB" - }, - "source": [ - "You can of course go ahead and confirm (using prep+bake) that the recipe will work as you expect it - all the cuisine labels having `559` observations.\n", - "\n", - "Since we'll be using this recipe as a preprocessor for modeling, a `workflow()` will do all the prep and bake for us, so we won't have to manually estimate the recipe.\n", - "\n", - "Now we are ready to train a model 👩‍💻👨‍💻!\n", - "\n", - "## 3. Choosing your classifier\n", - "\n", - "

\n", - " \n", - "

Artwork by @allison_horst
\n" - ] + } }, { "cell_type": "markdown", + "source": [ + "Now we have to decide which algorithm to use for the job 🤔.\r\n", + "\r\n", + "In Tidymodels, the [`parsnip package`](https://parsnip.tidymodels.org/index.html) provides consistent interface for working with models across different engines (packages). Please see the parsnip documentation to explore [model types & engines](https://www.tidymodels.org/find/parsnip/#models) and their corresponding [model arguements](https://www.tidymodels.org/find/parsnip/#model-args). The variety is quite bewildering at first sight. For instance, the following methods all include classification techniques:\r\n", + "\r\n", + "- C5.0 Rule-Based Classification Models\r\n", + "\r\n", + "- Flexible Discriminant Models\r\n", + "\r\n", + "- Linear Discriminant Models\r\n", + "\r\n", + "- Regularized Discriminant Models\r\n", + "\r\n", + "- Logistic Regression Models\r\n", + "\r\n", + "- Multinomial Regression Models\r\n", + "\r\n", + "- Naive Bayes Models\r\n", + "\r\n", + "- Support Vector Machines\r\n", + "\r\n", + "- Nearest Neighbors\r\n", + "\r\n", + "- Decision Trees\r\n", + "\r\n", + "- Ensemble methods\r\n", + "\r\n", + "- Neural Networks\r\n", + "\r\n", + "The list goes on!\r\n", + "\r\n", + "### **What classifier to go with?**\r\n", + "\r\n", + "So, which classifier should you choose? Often, running through several and looking for a good result is a way to test.\r\n", + "\r\n", + "> AutoML solves this problem neatly by running these comparisons in the cloud, allowing you to choose the best algorithm for your data. Try it [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)\r\n", + "\r\n", + "Also the choice of classifier depends on our problem. For instance, when the outcome can be categorized into `more than two classes`, like in our case, you must use a `multiclass classification algorithm` as opposed to `binary classification.`\r\n", + "\r\n", + "### **A better approach**\r\n", + "\r\n", + "A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Here, we discover that, for our multiclass problem, we have some choices:\r\n", + "\r\n", + "

\r\n", + " \r\n", + "

A section of Microsoft's Algorithm Cheat Sheet, detailing multiclass classification options
\r\n", + "\r\n" + ], "metadata": { "id": "a6DLAZ3vJZ14" - }, - "source": [ - "Now we have to decide which algorithm to use for the job 🤔.\n", - "\n", - "In Tidymodels, the [`parsnip package`](https://parsnip.tidymodels.org/index.html) provides consistent interface for working with models across different engines (packages). Please see the parsnip documentation to explore [model types & engines](https://www.tidymodels.org/find/parsnip/#models) and their corresponding [model arguements](https://www.tidymodels.org/find/parsnip/#model-args). The variety is quite bewildering at first sight. For instance, the following methods all include classification techniques:\n", - "\n", - "- C5.0 Rule-Based Classification Models\n", - "\n", - "- Flexible Discriminant Models\n", - "\n", - "- Linear Discriminant Models\n", - "\n", - "- Regularized Discriminant Models\n", - "\n", - "- Logistic Regression Models\n", - "\n", - "- Multinomial Regression Models\n", - "\n", - "- Naive Bayes Models\n", - "\n", - "- Support Vector Machines\n", - "\n", - "- Nearest Neighbors\n", - "\n", - "- Decision Trees\n", - "\n", - "- Ensemble methods\n", - "\n", - "- Neural Networks\n", - "\n", - "The list goes on!\n", - "\n", - "### **What classifier to go with?**\n", - "\n", - "So, which classifier should you choose? Often, running through several and looking for a good result is a way to test.\n", - "\n", - "> AutoML solves this problem neatly by running these comparisons in the cloud, allowing you to choose the best algorithm for your data. Try it [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)\n", - "\n", - "Also the choice of classifier depends on our problem. For instance, when the outcome can be categorized into `more than two classes`, like in our case, you must use a `multiclass classification algorithm` as opposed to `binary classification.`\n", - "\n", - "### **A better approach**\n", - "\n", - "A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Here, we discover that, for our multiclass problem, we have some choices:\n", - "\n", - "

\n", - " \n", - "

A section of Microsoft's Algorithm Cheat Sheet, detailing multiclass classification options
\n", - "\n" - ] + } }, { "cell_type": "markdown", - "metadata": { - "id": "gWMsVcbBJemu" - }, "source": [ "### **Reasoning**\n", "\n", @@ -558,28 +651,23 @@ "> I picked a value for `penalty` sort of randomly. There are better ways to choose this value that is, by using `resampling` and `tuning` the model which we'll discuss later.\n", ">\n", "> See [Tidymodels: Get Started](https://www.tidymodels.org/start/tuning/) in case you want to learn more on how to tune model hyperparameters." - ] + ], + "metadata": { + "id": "gWMsVcbBJemu" + } }, { "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 166 - }, - "id": "Wq_fcyQiJvfG", - "outputId": "c30449c7-3864-4be7-f810-72a003743e2d" - }, + "execution_count": 6, "source": [ - "# Create a multinomial regression model specification\n", - "mr_spec <- multinom_reg(penalty = 1) %>% \n", - " set_engine(\"nnet\", MaxNWts = 2086) %>% \n", - " set_mode(\"classification\")\n", - "\n", - "# Print model specification\n", + "# Create a multinomial regression model specification\r\n", + "mr_spec <- multinom_reg(penalty = 1) %>% \r\n", + " set_engine(\"nnet\", MaxNWts = 2086) %>% \r\n", + " set_mode(\"classification\")\r\n", + "\r\n", + "# Print model specification\r\n", "mr_spec" ], - "execution_count": 6, "outputs": [ { "output_type": "display_data", @@ -598,39 +686,39 @@ }, "metadata": {} } - ] + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 166 + }, + "id": "Wq_fcyQiJvfG", + "outputId": "c30449c7-3864-4be7-f810-72a003743e2d" + } }, { "cell_type": "markdown", - "metadata": { - "id": "NlSbzDfgJ0zh" - }, "source": [ "Great job 🥳! Now that we have a recipe and a model specification, we need to find a way of bundling them together into an object that will first preprocess the data then fit the model on the preprocessed data and also allow for potential post-processing activities. In Tidymodels, this convenient object is called a [`workflow`](https://workflows.tidymodels.org/) and conveniently holds your modeling components! This is what we'd call *pipelines* in *Python*.\n", "\n", "So let's bundle everything up into a workflow!📦" - ] + ], + "metadata": { + "id": "NlSbzDfgJ0zh" + } }, { "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 333 - }, - "id": "Sc1TfPA4Ke3_", - "outputId": "82c70013-e431-4e7e-cef6-9fcf8aad4a6c" - }, + "execution_count": 7, "source": [ - "# Bundle recipe and model specification\n", - "mr_wf <- workflow() %>% \n", - " add_recipe(cuisines_recipe) %>% \n", - " add_model(mr_spec)\n", - "\n", - "# Print out workflow\n", + "# Bundle recipe and model specification\r\n", + "mr_wf <- workflow() %>% \r\n", + " add_recipe(cuisines_recipe) %>% \r\n", + " add_model(mr_spec)\r\n", + "\r\n", + "# Print out workflow\r\n", "mr_wf" ], - "execution_count": 7, "outputs": [ { "output_type": "display_data", @@ -659,34 +747,34 @@ }, "metadata": {} } - ] + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 333 + }, + "id": "Sc1TfPA4Ke3_", + "outputId": "82c70013-e431-4e7e-cef6-9fcf8aad4a6c" + } }, { "cell_type": "markdown", - "metadata": { - "id": "TNQ8i85aKf9L" - }, "source": [ "Workflows 👌👌! A **`workflow()`** can be fit in much the same way a model can. So, time to train a model!" - ] + ], + "metadata": { + "id": "TNQ8i85aKf9L" + } }, { "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "GMbdfVmTKkJI", - "outputId": "adf9ebdf-d69d-4a64-e9fd-e06e5322292e" - }, + "execution_count": 8, "source": [ "# Train a multinomial regression model\n", "mr_fit <- fit(object = mr_wf, data = cuisines_train)\n", "\n", "mr_fit" ], - "execution_count": 8, "outputs": [ { "output_type": "display_data", @@ -759,31 +847,32 @@ }, "metadata": {} } - ] + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "GMbdfVmTKkJI", + "outputId": "adf9ebdf-d69d-4a64-e9fd-e06e5322292e" + } }, { "cell_type": "markdown", - "metadata": { - "id": "tt2BfOxrKmcJ" - }, "source": [ "The output shows the coefficients that the model learned during training.\n", "\n", "### Evaluate the Trained Model\n", "\n", "It's time to see how the model performed 📏 by evaluating it on a test set! Let's begin by making predictions on the test set." - ] + ], + "metadata": { + "id": "tt2BfOxrKmcJ" + } }, { "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 248 - }, - "id": "CqtckvtsKqax", - "outputId": "e57fe557-6a68-4217-fe82-173328c5436d" - }, + "execution_count": 9, "source": [ "# Make predictions on the test set\n", "results <- cuisines_test %>% select(cuisine) %>% \n", @@ -793,7 +882,6 @@ "results %>% \n", " slice_head(n = 5)" ], - "execution_count": 9, "outputs": [ { "output_type": "display_data", @@ -806,8 +894,32 @@ "4 indian indian \n", "5 indian indian " ], - "text/latex": "A tibble: 5 × 2\n\\begin{tabular}{ll}\n cuisine & .pred\\_class\\\\\n & \\\\\n\\hline\n\t indian & thai \\\\\n\t indian & indian\\\\\n\t indian & indian\\\\\n\t indian & indian\\\\\n\t indian & indian\\\\\n\\end{tabular}\n", - "text/markdown": "\nA tibble: 5 × 2\n\n| cuisine <fct> | .pred_class <fct> |\n|---|---|\n| indian | thai |\n| indian | indian |\n| indian | indian |\n| indian | indian |\n| indian | indian |\n\n", + "text/markdown": [ + "\n", + "A tibble: 5 × 2\n", + "\n", + "| cuisine <fct> | .pred_class <fct> |\n", + "|---|---|\n", + "| indian | thai |\n", + "| indian | indian |\n", + "| indian | indian |\n", + "| indian | indian |\n", + "| indian | indian |\n", + "\n" + ], + "text/latex": [ + "A tibble: 5 × 2\n", + "\\begin{tabular}{ll}\n", + " cuisine & .pred\\_class\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t indian & thai \\\\\n", + "\t indian & indian\\\\\n", + "\t indian & indian\\\\\n", + "\t indian & indian\\\\\n", + "\t indian & indian\\\\\n", + "\\end{tabular}\n" + ], "text/html": [ "
A tibble: 5 × 2
\n", "\n", @@ -827,32 +939,32 @@ }, "metadata": {} } - ] + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 248 + }, + "id": "CqtckvtsKqax", + "outputId": "e57fe557-6a68-4217-fe82-173328c5436d" + } }, { "cell_type": "markdown", - "metadata": { - "id": "8w5N6XsBKss7" - }, "source": [ "Great job! In Tidymodels, evaluating model performance can be done using [yardstick](https://yardstick.tidymodels.org/) - a package used to measure the effectiveness of models using performance metrics. As we did in our logistic regression lesson, let's begin by computing a confusion matrix." - ] + ], + "metadata": { + "id": "8w5N6XsBKss7" + } }, { "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 133 - }, - "id": "YvODvsLkK0iG", - "outputId": "bb69da84-1266-47ad-b174-d43b88ca2988" - }, + "execution_count": 10, "source": [ "# Confusion matrix for categorical data\n", "conf_mat(data = results, truth = cuisine, estimate = .pred_class)\n" ], - "execution_count": 10, "outputs": [ { "output_type": "display_data", @@ -869,27 +981,28 @@ }, "metadata": {} } - ] + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 133 + }, + "id": "YvODvsLkK0iG", + "outputId": "bb69da84-1266-47ad-b174-d43b88ca2988" + } }, { "cell_type": "markdown", - "metadata": { - "id": "c0HfPL16Lr6U" - }, "source": [ "When dealing with multiple classes, it's generally more intuitive to visualize this as a heat map, like this:" - ] + ], + "metadata": { + "id": "c0HfPL16Lr6U" + } }, { "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 436 - }, - "id": "HsAtwukyLsvt", - "outputId": "3032a224-a2c8-4270-b4f2-7bb620317400" - }, + "execution_count": 11, "source": [ "update_geom_defaults(geom = \"tile\", new = list(color = \"black\", alpha = 0.7))\n", "# Visualize confusion matrix\n", @@ -897,15 +1010,14 @@ " conf_mat(cuisine, .pred_class) %>% \n", " autoplot(type = \"heatmap\")" ], - "execution_count": 11, "outputs": [ { "output_type": "display_data", "data": { - "image/png": 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ckGJBWQZECyAUkFJBmQbEBSAUkGJBuQ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\nbUxUeFu/9LzFHWrTkBa0SfZ2NiA96ab6g2XuRiDt1DPOHfWbEOY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" }, "metadata": { "image/png": { @@ -914,35 +1026,35 @@ } } } - ] + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 436 + }, + "id": "HsAtwukyLsvt", + "outputId": "3032a224-a2c8-4270-b4f2-7bb620317400" + } }, { "cell_type": "markdown", - "metadata": { - "id": "oOJC87dkLwPr" - }, "source": [ "The darker squares in the confusion matrix plot indicate high numbers of cases, and you can hopefully see a diagonal line of darker squares indicating cases where the predicted and actual label are the same.\n", "\n", "Let's now calculate summary statistics for the confusion matrix." - ] + ], + "metadata": { + "id": "oOJC87dkLwPr" + } }, { "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 494 - }, - "id": "OYqetUyzL5Wz", - "outputId": "6a84d65e-113d-4281-dfc1-16e8b70f37e6" - }, + "execution_count": 12, "source": [ "# Summary stats for confusion matrix\n", "conf_mat(data = results, truth = cuisine, estimate = .pred_class) %>% \n", "summary()" ], - "execution_count": 12, "outputs": [ { "output_type": "display_data", @@ -963,8 +1075,48 @@ "12 recall macro 0.7780927\n", "13 f_meas macro 0.7641862" ], - "text/latex": "A tibble: 13 × 3\n\\begin{tabular}{lll}\n .metric & .estimator & .estimate\\\\\n & & \\\\\n\\hline\n\t accuracy & multiclass & 0.7880435\\\\\n\t kap & multiclass & 0.7276583\\\\\n\t sens & macro & 0.7780927\\\\\n\t spec & macro & 0.9477598\\\\\n\t ppv & macro & 0.7585583\\\\\n\t npv & macro & 0.9460080\\\\\n\t mcc & multiclass & 0.7292724\\\\\n\t j\\_index & macro & 0.7258524\\\\\n\t bal\\_accuracy & macro & 0.8629262\\\\\n\t detection\\_prevalence & macro & 0.2000000\\\\\n\t precision & macro & 0.7585583\\\\\n\t recall & macro & 0.7780927\\\\\n\t f\\_meas & macro & 0.7641862\\\\\n\\end{tabular}\n", - "text/markdown": "\nA tibble: 13 × 3\n\n| .metric <chr> | .estimator <chr> | .estimate <dbl> |\n|---|---|---|\n| accuracy | multiclass | 0.7880435 |\n| kap | multiclass | 0.7276583 |\n| sens | macro | 0.7780927 |\n| spec | macro | 0.9477598 |\n| ppv | macro | 0.7585583 |\n| npv | macro | 0.9460080 |\n| mcc | multiclass | 0.7292724 |\n| j_index | macro | 0.7258524 |\n| bal_accuracy | macro | 0.8629262 |\n| detection_prevalence | macro | 0.2000000 |\n| precision | macro | 0.7585583 |\n| recall | macro | 0.7780927 |\n| f_meas | macro | 0.7641862 |\n\n", + "text/markdown": [ + "\n", + "A tibble: 13 × 3\n", + "\n", + "| .metric <chr> | .estimator <chr> | .estimate <dbl> |\n", + "|---|---|---|\n", + "| accuracy | multiclass | 0.7880435 |\n", + "| kap | multiclass | 0.7276583 |\n", + "| sens | macro | 0.7780927 |\n", + "| spec | macro | 0.9477598 |\n", + "| ppv | macro | 0.7585583 |\n", + "| npv | macro | 0.9460080 |\n", + "| mcc | multiclass | 0.7292724 |\n", + "| j_index | macro | 0.7258524 |\n", + "| bal_accuracy | macro | 0.8629262 |\n", + "| detection_prevalence | macro | 0.2000000 |\n", + "| precision | macro | 0.7585583 |\n", + "| recall | macro | 0.7780927 |\n", + "| f_meas | macro | 0.7641862 |\n", + "\n" + ], + "text/latex": [ + "A tibble: 13 × 3\n", + "\\begin{tabular}{lll}\n", + " .metric & .estimator & .estimate\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t accuracy & multiclass & 0.7880435\\\\\n", + "\t kap & multiclass & 0.7276583\\\\\n", + "\t sens & macro & 0.7780927\\\\\n", + "\t spec & macro & 0.9477598\\\\\n", + "\t ppv & macro & 0.7585583\\\\\n", + "\t npv & macro & 0.9460080\\\\\n", + "\t mcc & multiclass & 0.7292724\\\\\n", + "\t j\\_index & macro & 0.7258524\\\\\n", + "\t bal\\_accuracy & macro & 0.8629262\\\\\n", + "\t detection\\_prevalence & macro & 0.2000000\\\\\n", + "\t precision & macro & 0.7585583\\\\\n", + "\t recall & macro & 0.7780927\\\\\n", + "\t f\\_meas & macro & 0.7641862\\\\\n", + "\\end{tabular}\n" + ], "text/html": [ "
A tibble: 5 × 2
\n", "\n", @@ -992,13 +1144,18 @@ }, "metadata": {} } - ] + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 494 + }, + "id": "OYqetUyzL5Wz", + "outputId": "6a84d65e-113d-4281-dfc1-16e8b70f37e6" + } }, { "cell_type": "markdown", - "metadata": { - "id": "43t7vz8vMJtW" - }, "source": [ "If we narrow down to some metrics such as accuracy, sensitivity, ppv, we are not badly off for a start 🥳!\n", "\n", @@ -1009,18 +1166,14 @@ "Well, Statistical machine learning algorithms, like logistic regression, are based on `probability`; so what actually gets predicted by a classifier is a probability distribution over a set of possible outcomes. The class with the highest probability is then chosen as the most likely outcome for the given observations.\n", "\n", "Let's see this in action by making both hard class predictions and probabilities." - ] + ], + "metadata": { + "id": "43t7vz8vMJtW" + } }, { "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 248 - }, - "id": "xdKNs-ZPMTJL", - "outputId": "68f6ac5a-725a-4eff-9ea6-481fef00e008" - }, + "execution_count": 13, "source": [ "# Make hard class prediction and probabilities\n", "results_prob <- cuisines_test %>%\n", @@ -1032,7 +1185,6 @@ "results_prob %>% \n", " slice_head(n = 5)" ], - "execution_count": 13, "outputs": [ { "output_type": "display_data", @@ -1051,8 +1203,32 @@ "4 3.863391e-03\n", "5 5.653283e-03" ], - "text/latex": "A tibble: 5 × 7\n\\begin{tabular}{lllllll}\n cuisine & .pred\\_class & .pred\\_chinese & .pred\\_indian & .pred\\_japanese & .pred\\_korean & .pred\\_thai\\\\\n & & & & & & \\\\\n\\hline\n\t indian & thai & 1.551259e-03 & 0.4587877 & 5.988039e-04 & 2.428503e-04 & 5.388194e-01\\\\\n\t indian & indian & 2.637133e-05 & 0.9999488 & 6.648651e-07 & 2.259993e-05 & 1.577948e-06\\\\\n\t indian & indian & 1.049433e-03 & 0.9909982 & 1.060937e-03 & 1.644947e-05 & 6.874989e-03\\\\\n\t indian & indian & 6.237482e-02 & 0.4763035 & 9.136702e-02 & 3.660913e-01 & 3.863391e-03\\\\\n\t indian & indian & 1.431745e-02 & 0.9418551 & 2.945239e-02 & 8.721782e-03 & 5.653283e-03\\\\\n\\end{tabular}\n", - "text/markdown": "\nA tibble: 5 × 7\n\n| cuisine <fct> | .pred_class <fct> | .pred_chinese <dbl> | .pred_indian <dbl> | .pred_japanese <dbl> | .pred_korean <dbl> | .pred_thai <dbl> |\n|---|---|---|---|---|---|---|\n| indian | thai | 1.551259e-03 | 0.4587877 | 5.988039e-04 | 2.428503e-04 | 5.388194e-01 |\n| indian | indian | 2.637133e-05 | 0.9999488 | 6.648651e-07 | 2.259993e-05 | 1.577948e-06 |\n| indian | indian | 1.049433e-03 | 0.9909982 | 1.060937e-03 | 1.644947e-05 | 6.874989e-03 |\n| indian | indian | 6.237482e-02 | 0.4763035 | 9.136702e-02 | 3.660913e-01 | 3.863391e-03 |\n| indian | indian | 1.431745e-02 | 0.9418551 | 2.945239e-02 | 8.721782e-03 | 5.653283e-03 |\n\n", + "text/markdown": [ + "\n", + "A tibble: 5 × 7\n", + "\n", + "| cuisine <fct> | .pred_class <fct> | .pred_chinese <dbl> | .pred_indian <dbl> | .pred_japanese <dbl> | .pred_korean <dbl> | .pred_thai <dbl> |\n", + "|---|---|---|---|---|---|---|\n", + "| indian | thai | 1.551259e-03 | 0.4587877 | 5.988039e-04 | 2.428503e-04 | 5.388194e-01 |\n", + "| indian | indian | 2.637133e-05 | 0.9999488 | 6.648651e-07 | 2.259993e-05 | 1.577948e-06 |\n", + "| indian | indian | 1.049433e-03 | 0.9909982 | 1.060937e-03 | 1.644947e-05 | 6.874989e-03 |\n", + "| indian | indian | 6.237482e-02 | 0.4763035 | 9.136702e-02 | 3.660913e-01 | 3.863391e-03 |\n", + "| indian | indian | 1.431745e-02 | 0.9418551 | 2.945239e-02 | 8.721782e-03 | 5.653283e-03 |\n", + "\n" + ], + "text/latex": [ + "A tibble: 5 × 7\n", + "\\begin{tabular}{lllllll}\n", + " cuisine & .pred\\_class & .pred\\_chinese & .pred\\_indian & .pred\\_japanese & .pred\\_korean & .pred\\_thai\\\\\n", + " & & & & & & \\\\\n", + "\\hline\n", + "\t indian & thai & 1.551259e-03 & 0.4587877 & 5.988039e-04 & 2.428503e-04 & 5.388194e-01\\\\\n", + "\t indian & indian & 2.637133e-05 & 0.9999488 & 6.648651e-07 & 2.259993e-05 & 1.577948e-06\\\\\n", + "\t indian & indian & 1.049433e-03 & 0.9909982 & 1.060937e-03 & 1.644947e-05 & 6.874989e-03\\\\\n", + "\t indian & indian & 6.237482e-02 & 0.4763035 & 9.136702e-02 & 3.660913e-01 & 3.863391e-03\\\\\n", + "\t indian & indian & 1.431745e-02 & 0.9418551 & 2.945239e-02 & 8.721782e-03 & 5.653283e-03\\\\\n", + "\\end{tabular}\n" + ], "text/html": [ "
A tibble: 13 × 3
\n", "\n", @@ -1072,13 +1248,18 @@ }, "metadata": {} } - ] + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 248 + }, + "id": "xdKNs-ZPMTJL", + "outputId": "68f6ac5a-725a-4eff-9ea6-481fef00e008" + } }, { "cell_type": "markdown", - "metadata": { - "id": "2tWVHMeLMYdM" - }, "source": [ "Much better!\n", "\n", @@ -1102,7 +1283,10 @@ "Happy Learning,\n", "\n", "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n" - ] + ], + "metadata": { + "id": "2tWVHMeLMYdM" + } } ] } \ No newline at end of file diff --git a/4-Classification/2-Classifiers-1/solution/lesson_11.Rmd b/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd similarity index 99% rename from 4-Classification/2-Classifiers-1/solution/lesson_11.Rmd rename to 4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd index f7dab06a..a4221217 100644 --- a/4-Classification/2-Classifiers-1/solution/lesson_11.Rmd +++ b/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd @@ -154,7 +154,7 @@ Now we are ready to train a model 👩‍💻👨‍💻! ## 3. Choosing your classifier -![Artwork by \@allison_horst](../images/parsnip.jpg){width="600"} +![Artwork by \@allison_horst](../../images/parsnip.jpg){width="600"} Now we have to decide which algorithm to use for the job 🤔. @@ -198,7 +198,7 @@ Also the choice of classifier depends on our problem. For instance, when the out A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Here, we discover that, for our multiclass problem, we have some choices: -![A section of Microsoft's Algorithm Cheat Sheet, detailing multiclass classification options](../images/cheatsheet.png){width="500"} +![A section of Microsoft's Algorithm Cheat Sheet, detailing multiclass classification options](../../images/cheatsheet.png){width="500"} ### **Reasoning**
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