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katieburak committed Jul 11, 2024
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<li class="toctree-l1"><a class="reference internal" href="notes/day2.html">Day 2 - Girls in Data Science</a></li>



<li class="toctree-l1"><a class="reference internal" href="notes/day3.html">Day 3 - Girls in Data Science</a></li>


Expand Down Expand Up @@ -415,7 +416,7 @@ <h2>Topic Overview<a class="headerlink" href="#topic-overview" title="Permalink
</tr>
</tbody>
</table>
<p>A Jupyter notebook of the material is available <a class="reference external" href="https://katieburak.github.io/girls-in-DS/notes/day2.html">here</a>.</p>
<p>A Jupyter notebook of the material is available <a class="reference external" href="https://katieburak.github.io/girls-in-DS/README.html">here</a>.</p>
</section>
<section id="references">
<h2>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h2>
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Expand Up @@ -22,7 +22,7 @@ Lunch will be provided each day for the participants.
| Day 2 | Measures of central tendency and spread, statistical inference and sampling, observational studies vs. experiments |
| Day 3 | Machine learning fundamentals, answering predictive questions (regression, classification) |

A Jupyter notebook of the material is available [here](https://katieburak.github.io/girls-in-DS/notes/day2.html).
A Jupyter notebook of the material is available [here](https://katieburak.github.io/girls-in-DS/README.html).

## References

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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?"
]
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"## 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"
]
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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."
]
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{
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"## 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",
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"## 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",
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"## 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",
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"## 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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"## 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"
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"\n"
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"Showing your classifier the labels of evaluation data is like cheating on a test; it'll look more accurate than it really is."
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"#### 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`."
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<li class="toctree-l1"><a class="reference internal" href="notes/day2.html">Day 2 - Girls in Data Science</a></li>



<li class="toctree-l1"><a class="reference internal" href="notes/day3.html">Day 3 - Girls in Data Science</a></li>


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