msb-public
/
ML-For-Beginners
mirror of https://github.com/microsoft/ML-For-Beginners.git
1
0
Code Issues Projects Releases Wiki Activity

Explain what tibble is

Browse Source
pull/313/head
R-icntay 3 years ago
parent 558f25f80d
commit 74bf75f37f
2 changed files with 51 additions and 47 deletions
Show Stats Download Patch File Download Diff File

90
4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
Unescape View File

@ -163,12 +163,12 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Basic information about the data\n",
"df %>%\n",
" introduce()\n",
"\n",
"# Visualize basic information above\n",
"df %>% \n",
"# Basic information about the data\r\n",
"df %>%\r\n",
" introduce()\r\n",
"\r\n",
"# Visualize basic information above\r\n",
"df %>% \r\n",
" plot_intro(ggtheme = theme_light())"
],
"outputs": [],
@ -193,17 +193,17 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Count observations per cuisine\n",
"df %>% \n",
" count(cuisine) %>% \n",
" arrange(n)\n",
"\n",
"# Plot the distribution\n",
"theme_set(theme_light())\n",
"df %>% \n",
" count(cuisine) %>% \n",
" ggplot(mapping = aes(x = n, y = reorder(cuisine, -n))) +\n",
" geom_col(fill = \"midnightblue\", alpha = 0.7) +\n",
"# Count observations per cuisine\r\n",
"df %>% \r\n",
" count(cuisine) %>% \r\n",
" arrange(n)\r\n",
"\r\n",
"# Plot the distribution\r\n",
"theme_set(theme_light())\r\n",
"df %>% \r\n",
" count(cuisine) %>% \r\n",
" ggplot(mapping = aes(x = n, y = reorder(cuisine, -n))) +\r\n",
" geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
" ylab(\"cuisine\")"
],
"outputs": [],
@ -214,15 +214,17 @@
{
"cell_type": "markdown",
"source": [
"There are a finite number of cuisines, but the distribution of data is uneven. You can fix that! Before doing so, explore a little more.\n",
"\n",
"Next, let's assign each cuisine into its individual table and find out how much data is available (rows, columns) per cuisine.\n",
"\n",
"<p >\n",
" <img src=\"../../images/dplyr_filter.jpg\"\n",
" width=\"600\"/>\n",
" <figcaption>Artwork by @allison_horst</figcaption>\n",
"\n"
"There are a finite number of cuisines, but the distribution of data is uneven. You can fix that! Before doing so, explore a little more.\r\n",
"\r\n",
"Next, let's assign each cuisine into its individual tibble and find out how much data is available (rows, columns) per cuisine.\r\n",
"\r\n",
"> A tibble is a modern reimagining of the data frame, keeping what time has proven to be effective, and throwing out what is not.\r\n",
"\r\n",
"<p >\r\n",
" <img src=\"../../images/dplyr_filter.jpg\"\r\n",
" width=\"600\"/>\r\n",
" <figcaption>Artwork by @allison_horst</figcaption>\r\n",
"\r\n"
],
"metadata": {
"id": "vVvyDb1kG2in"
@ -232,24 +234,24 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Create individual tables for the cuisines\n",
"thai_df <- df %>% \n",
" filter(cuisine == \"thai\")\n",
"japanese_df <- df %>% \n",
" filter(cuisine == \"japanese\")\n",
"chinese_df <- df %>% \n",
" filter(cuisine == \"chinese\")\n",
"indian_df <- df %>% \n",
" filter(cuisine == \"indian\")\n",
"korean_df <- df %>% \n",
" filter(cuisine == \"korean\")\n",
"\n",
"\n",
"# Find out how much data is avilable per cuisine\n",
"cat(\" thai df:\", dim(thai_df), \"\\n\",\n",
" \"japanese df:\", dim(japanese_df), \"\\n\",\n",
" \"chinese_df:\", dim(chinese_df), \"\\n\",\n",
" \"indian_df:\", dim(indian_df), \"\\n\",\n",
"# Create individual tibble for the cuisines\r\n",
"thai_df <- df %>% \r\n",
" filter(cuisine == \"thai\")\r\n",
"japanese_df <- df %>% \r\n",
" filter(cuisine == \"japanese\")\r\n",
"chinese_df <- df %>% \r\n",
" filter(cuisine == \"chinese\")\r\n",
"indian_df <- df %>% \r\n",
" filter(cuisine == \"indian\")\r\n",
"korean_df <- df %>% \r\n",
" filter(cuisine == \"korean\")\r\n",
"\r\n",
"\r\n",
"# Find out how much data is avilable per cuisine\r\n",
"cat(\" thai df:\", dim(thai_df), \"\\n\",\r\n",
" \"japanese df:\", dim(japanese_df), \"\\n\",\r\n",
" \"chinese_df:\", dim(chinese_df), \"\\n\",\r\n",
" \"indian_df:\", dim(indian_df), \"\\n\",\r\n",
" \"korean_df:\", dim(korean_df))"
],
"outputs": [],

8
4-Classification/1-Introduction/solution/R/lesson_10.Rmd
Unescape View File

@ -34,7 +34,7 @@ Classification is one of the fundamental activities of the machine learning rese
To state the process in a more scientific way, your classification method creates a predictive model that enables you to map the relationship between input variables to output variables.
![Binary vs. multiclass problems for classification algorithms to handle. Infographic by Jen Looper](../images/binary-multiclass.png)
![Binary vs. multiclass problems for classification algorithms to handle. Infographic by Jen Looper](../../images/binary-multiclass.png){width="500"}
Before starting the process of cleaning our data, visualizing it, and prepping it for our ML tasks, let's learn a bit about the various ways machine learning can be leveraged to classify data.
@ -127,7 +127,9 @@ There are a finite number of cuisines, but the distribution of data is uneven. Y
2. Next, let's assign each cuisine into it's individual tibble and find out how much data is available (rows, columns) per cuisine.
![Artwork by \@allison_horst](../images/dplyr_filter.jpg)
> A tibble, or tbl_df, is a modern reimagining of the data.frame, keeping what time has proven to be effective, and throwing out what is not.
![Artwork by \@allison_horst](../../images/dplyr_filter.jpg)
```{r cuisine_df}
# Create individual tibbles for the cuisines
@ -297,7 +299,7 @@ df_select %>%
## Preprocessing data using recipes 👩‍🍳👨‍🍳 - Dealing with imbalanced data ⚖️
![Artwork by \@allison_horst](../images/recipes.png)
![Artwork by \@allison_horst](../../images/recipes.png)
Given that this lesson is about cuisines, we have to put `recipes` into context .

Write Preview
Loading…
Cancel
Save