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katieburak committed Jul 10, 2024
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256 changes: 175 additions & 81 deletions notes/.ipynb_checkpoints/day3-checkpoint.ipynb
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2 changes: 1 addition & 1 deletion notes/.jupyter/desktop-workspaces/default-37a8.jupyterlab-workspace
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218 changes: 155 additions & 63 deletions notes/day3.ipynb
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"\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n",
"\u001b[31m✖\u001b[39m \u001b[34myardstick\u001b[39m::\u001b[32mspec()\u001b[39m masks \u001b[34mreadr\u001b[39m::spec()\n",
"\u001b[31m✖\u001b[39m \u001b[34mrecipes\u001b[39m::\u001b[32mstep()\u001b[39m masks \u001b[34mstats\u001b[39m::step()\n",
"\u001b[34m•\u001b[39m Dig deeper into tidy modeling with R at \u001b[32mhttps://www.tmwr.org\u001b[39m\n",
"\u001b[34m•\u001b[39m Learn how to get started at \u001b[32mhttps://www.tidymodels.org/start/\u001b[39m\n",
"\n",
"\n",
"Attaching package: ‘palmerpenguins’\n",
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"Descriptive, exploratory, predictive, inferential, causal, or mechanistic?"
]
},
{
"cell_type": "markdown",
"id": "4d1ad8c2-49ed-44f0-84f3-3948d5fd1408",
"metadata": {
"tags": []
},
"source": [
"## 1.2 Training and Test Sets\n",
"\n",
"When building a machine learning model, typically we start by dividing our data into two sets:\n",
"1) Training set\n",
"2) Test set\n",
" \n",
"The **training set** is a subset of our data that is used is to train or teach our model to perform sort of predictive task. Then, using our **test set**, we can evaluate how well our model performs on unseen data. \n"
]
},
{
"cell_type": "markdown",
"id": "fe206660-2ee8-4c43-9d6d-c3339f3cf870",
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"There are two important things to do when splitting data.\n",
"\n",
"1. **Shuffling:** randomly reorder the data before splitting\n",
"2. **Stratification:** make sure the two split subsets of data have roughly equal proportions of the different labels\n",
"\n",
"<center>\n",
"<img src=\"https://datasciencebook.ca/img/classification2/training_test.png\" width=\"1100\"/>\n",
"</center>\n",
"\n",
"#### Golden Rule of Machine Learning / Statistics:\n",
"\n",
"**Don't use your testing data to train your model!**\n",
"\n",
"Showing your classifier the labels of evaluation data is like cheating on a test; it'll look more accurate than it really is."
]
},
{
"cell_type": "markdown",
"id": "3a604659-c9c4-4b17-9f1a-8c7f5cb8d3a8",
"metadata": {},
"source": [
"## 1.2 K-nearest neighbours classification\n",
"## 1.3 K-nearest neighbours classification\n",
"\n",
"*Predict the label / class for a new observation using the K closest points from our dataset.*\n"
]
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"id": "5de05126-4608-4a1b-8f3b-dcedb36821d0",
"metadata": {},
"source": [
"## 1.3 Standardizing Data\n",
"## 1.4 Standardizing Data\n",
"<center><img src=\"img/scaling_example1.png\" width=\"600\"/></center>"
]
},
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"id": "e27ffaac-1825-407a-8bd5-2dbc29006566",
"metadata": {},
"source": [
"## 1.4 `tidymodels` package in R\n",
"## 1.5 `tidymodels` package in R\n",
"\n",
"`tidymodels` is a collection of packages and handles computing distances, standardization, balancing, and prediction for us!\n",
"\n",
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"id": "8a5c9ef2-9bb3-4f3c-a1cc-4fcd15728997",
"metadata": {},
"source": [
"## 1.5 How to measure classifier performance?\n",
"## 1.6 How to measure classifier performance?\n",
"</br>\n",
"\n",
"### Accuracy\n",
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"We'll now talk about these steps below."
]
},
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"cell_type": "markdown",
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"## 1.6 Training and Test Sets\n",
"\n",
"When building a machine learning model, typically we start by dividing our data into two sets:\n",
"1) Training set\n",
"2) Test set\n",
" \n",
"The **training set** is a subset of our data that is used is to train or teach our model to perform sort of predictive task. Then, using our **test set**, we can evaluate how well our model performs on unseen data. \n"
]
},
{
"cell_type": "markdown",
"id": "905177de-cdd2-46dc-b468-08dfdf6c0ad3",
"metadata": {},
"source": [
"There are two important things to do when splitting data.\n",
"\n",
"1. **Shuffling:** randomly reorder the data before splitting\n",
"2. **Stratification:** make sure the two split subsets of data have roughly equal proportions of the different labels\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "59937715-de40-404f-b9f7-40249fc10dc9",
"metadata": {},
"source": [
"\n",
"<center>\n",
"<img src=\"https://datasciencebook.ca/img/classification2/training_test.png\" width=\"1100\"/>\n",
"</center>"
]
},
{
"cell_type": "markdown",
"id": "278edce1-f89a-4fb3-835e-9ce6cb077c7d",
"metadata": {},
"source": [
"#### Golden Rule of Machine Learning / Statistics:\n",
"\n",
"**Don't use your testing data to train your model!**\n",
"\n",
"Showing your classifier the labels of evaluation data is like cheating on a test; it'll look more accurate than it really is."
]
},
{
"cell_type": "markdown",
"id": "cf171c32-67c8-474a-9e8d-25f42b8aee43",
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"source": [
"#### Question\n",
"\n",
"What are the precision and recall for the classifier on the test data?"
"What are the precision and recall for the classifier on the test data?\n",
"\n",
"We can use the precision and recall functions from `tidymodels`."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "4da417d1-5f54-4332-95c1-b3f36e97eeeb",
"metadata": {},
"outputs": [
{
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"<table class=\"dataframe\">\n",
"<caption>A tibble: 1 × 3</caption>\n",
"<thead>\n",
"\t<tr><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>.estimate</th></tr>\n",
"\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
"</thead>\n",
"<tbody>\n",
"\t<tr><td>precision</td><td>binary</td><td>0.84</td></tr>\n",
"</tbody>\n",
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"A tibble: 1 × 3\n",
"\\begin{tabular}{lll}\n",
" .metric & .estimator & .estimate\\\\\n",
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"\n",
"A tibble: 1 × 3\n",
"\n",
"| .metric &lt;chr&gt; | .estimator &lt;chr&gt; | .estimate &lt;dbl&gt; |\n",
"|---|---|---|\n",
"| precision | binary | 0.84 |\n",
"\n"
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"tumor_test_predictions |>\n",
" precision(truth = Class, estimate = .pred_class, event_level = \"first\")"
]
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"<table class=\"dataframe\">\n",
"<caption>A tibble: 1 × 3</caption>\n",
"<thead>\n",
"\t<tr><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>.estimate</th></tr>\n",
"\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
"</thead>\n",
"<tbody>\n",
"\t<tr><td>recall</td><td>binary</td><td>0.7924528</td></tr>\n",
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"A tibble: 1 × 3\n",
"\\begin{tabular}{lll}\n",
" .metric & .estimator & .estimate\\\\\n",
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"\t recall & binary & 0.7924528\\\\\n",
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"\n",
"A tibble: 1 × 3\n",
"\n",
"| .metric &lt;chr&gt; | .estimator &lt;chr&gt; | .estimate &lt;dbl&gt; |\n",
"|---|---|---|\n",
"| recall | binary | 0.7924528 |\n",
"\n"
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" .metric .estimator .estimate\n",
"1 recall binary 0.7924528"
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"tumor_test_predictions |>\n",
" recall(truth = Class, estimate = .pred_class, event_level = \"first\")"
]
},
{
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},
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10 changes: 5 additions & 5 deletions worksheets/.ipynb_checkpoints/worksheet3-checkpoint.ipynb
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"metadata": {},
"source": [
"# Worksheet 3. Machine Learning\n",
"\n",
"Before getting started with the worksheet, please take a few minutes to fill out [this survey](https://forms.gle/z4JFsgJkhbiQ8PQZ9) about the camp. Thank you! \n",
"## Girls in Data Science Camp\n",
"\n",
"In this worksheet, we will be working with the [Nutrition Facts for McDonald's Menu](https://www.kaggle.com/datasets/mcdonalds/nutrition-facts?resource=download) data, which contains information about items sold at McDonald across 9 food categories. \n",
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"metadata": {},
"source": [
"#### 1.2 Removing Unwanted Columns\n",
"As discussed, let's exclude the `Item` and `Serving Size` columns from the dataset for the upcoming activities. Additionally, we will convert the `Category` column to the _factor_ class. \n",
"\n",
"Explain the distinctions between the `factor` and `character` classes in R, and illustrate the advantages of converting the `Category` column into a `factor`."
"As discussed, let's exclude the `Item` and `Serving Size` columns from the dataset for the upcoming activities. Additionally, we will convert the `Category` column to the _factor_ class (you can use `mutate(Category = as_factor(Category))`)."
]
},
{
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"id": "3c6ce735",
"metadata": {},
"source": [
"#### 2.3 Fit the Data into the Workflow\n",
"#### Fit the Data into the Workflow\n",
"Let's combine the recipe and model we previously defined to fit our `mcdonalds_train` data into a workflow. Follow these steps:\n",
"1. Initialize the workflow.\n",
"2. Specify the recipe.\n",
"3. Incloude the model.\n",
"3. Include the model.\n",
"4. Fit the training data."
]
},
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2 changes: 1 addition & 1 deletion worksheets/.jupyter/desktop-workspaces/default-37a8.jupyterlab-workspace
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8 changes: 3 additions & 5 deletions worksheets/worksheet3.ipynb
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"metadata": {},
"source": [
"#### 1.2 Removing Unwanted Columns\n",
"As discussed, let's exclude the `Item` and `Serving Size` columns from the dataset for the upcoming activities. Additionally, we will convert the `Category` column to the _factor_ class. \n",
"\n",
"Explain the distinctions between the `factor` and `character` classes in R, and illustrate the advantages of converting the `Category` column into a `factor`."
"As discussed, let's exclude the `Item` and `Serving Size` columns from the dataset for the upcoming activities. Additionally, we will convert the `Category` column to the _factor_ class (you can use `mutate(Category = as_factor(Category))`)."
]
},
{
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"id": "3c6ce735",
"metadata": {},
"source": [
"#### 2.3 Fit the Data into the Workflow\n",
"#### Fit the Data into the Workflow\n",
"Let's combine the recipe and model we previously defined to fit our `mcdonalds_train` data into a workflow. Follow these steps:\n",
"1. Initialize the workflow.\n",
"2. Specify the recipe.\n",
"3. Incloude the model.\n",
"3. Include the model.\n",
"4. Fit the training data."
]
},
Expand Down

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