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<a class="navbar-brand" href="week37-bs.html">Week 37: Statistical interpretations and Resampling Methods</a>
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<!-- navigation toc: --> <li><a href="._week37-bs001.html#plans-for-week-37-lecture-monday" style="font-size: 80%;">Plans for week 37, lecture Monday</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs002.html#plans-for-week-37-lab-sessions" style="font-size: 80%;">Plans for week 37, lab sessions</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs003.html#material-for-lecture-monday-september-9" style="font-size: 80%;">Material for lecture Monday September 9</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs004.html#deriving-ols-from-a-probability-distribution" style="font-size: 80%;">Deriving OLS from a probability distribution</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs005.html#independent-and-identically-distrubuted-iid" style="font-size: 80%;">Independent and Identically Distrubuted (iid)</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs006.html#maximum-likelihood-estimation-mle" style="font-size: 80%;">Maximum Likelihood Estimation (MLE)</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs007.html#a-new-cost-function" style="font-size: 80%;">A new Cost Function</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs008.html#more-basic-statistics-and-bayes-theorem" style="font-size: 80%;">More basic Statistics and Bayes' theorem</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs009.html#marginal-probability" style="font-size: 80%;">Marginal Probability</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs010.html#conditional-probability" style="font-size: 80%;">Conditional Probability</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs011.html#bayes-theorem" style="font-size: 80%;">Bayes' Theorem</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs012.html#interpretations-of-bayes-theorem" style="font-size: 80%;">Interpretations of Bayes' Theorem</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs013.html#example-of-usage-of-bayes-theorem" style="font-size: 80%;">Example of Usage of Bayes' theorem</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs014.html#doing-it-correctly" style="font-size: 80%;">Doing it correctly</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs015.html#bayes-theorem-and-ridge-and-lasso-regression" style="font-size: 80%;">Bayes' Theorem and Ridge and Lasso Regression</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs016.html#ridge-and-bayes" style="font-size: 80%;">Ridge and Bayes</a></li>
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<!-- navigation toc: --> <li><a href="._week37-bs021.html#why-resampling-methods" style="font-size: 80%;">Why resampling methods</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs023.html#resampling-methods" style="font-size: 80%;">Resampling methods</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs020.html#resampling-approaches-can-be-computationally-expensive" style="font-size: 80%;">Resampling approaches can be computationally expensive</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs021.html#why-resampling-methods" style="font-size: 80%;">Why resampling methods ?</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs022.html#statistical-analysis" style="font-size: 80%;">Statistical analysis</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs023.html#resampling-methods" style="font-size: 80%;">Resampling methods</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs024.html#resampling-methods-bootstrap" style="font-size: 80%;">Resampling methods: Bootstrap</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs025.html#the-central-limit-theorem" style="font-size: 80%;">The Central Limit Theorem</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs026.html#finding-the-limit" style="font-size: 80%;">Finding the Limit</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs027.html#rewriting-the-delta-function" style="font-size: 80%;">Rewriting the \( \delta \)-function</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs028.html#identifying-terms" style="font-size: 80%;">Identifying Terms</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs029.html#wrapping-it-up" style="font-size: 80%;">Wrapping it up</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs030.html#confidence-intervals" style="font-size: 80%;">Confidence Intervals</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs031.html#standard-approach-based-on-the-normal-distribution" style="font-size: 80%;">Standard Approach based on the Normal Distribution</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs032.html#resampling-methods-bootstrap-background" style="font-size: 80%;">Resampling methods: Bootstrap background</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs033.html#resampling-methods-more-bootstrap-background" style="font-size: 80%;">Resampling methods: More Bootstrap background</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs034.html#resampling-methods-bootstrap-approach" style="font-size: 80%;">Resampling methods: Bootstrap approach</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs035.html#resampling-methods-bootstrap-steps" style="font-size: 80%;">Resampling methods: Bootstrap steps</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs036.html#code-example-for-the-bootstrap-method" style="font-size: 80%;">Code example for the Bootstrap method</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs037.html#plotting-the-histogram" style="font-size: 80%;">Plotting the Histogram</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs038.html#the-bias-variance-tradeoff" style="font-size: 80%;">The bias-variance tradeoff</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs039.html#a-way-to-read-the-bias-variance-tradeoff" style="font-size: 80%;">A way to Read the Bias-Variance Tradeoff</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs040.html#example-code-for-bias-variance-tradeoff" style="font-size: 80%;">Example code for Bias-Variance tradeoff</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs041.html#understanding-what-happens" style="font-size: 80%;">Understanding what happens</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs042.html#summing-up" style="font-size: 80%;">Summing up</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs043.html#another-example-from-scikit-learn-s-repository" style="font-size: 80%;">Another Example from Scikit-Learn's Repository</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs044.html#various-steps-in-cross-validation" style="font-size: 80%;">Various steps in cross-validation</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs045.html#cross-validation-in-brief" style="font-size: 80%;">Cross-validation in brief</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs046.html#code-example-for-cross-validation-and-k-fold-cross-validation" style="font-size: 80%;">Code Example for Cross-validation and \( k \)-fold Cross-validation</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs047.html#more-examples-on-bootstrap-and-cross-validation-and-errors" style="font-size: 80%;">More examples on bootstrap and cross-validation and errors</a></li>
<!-- navigation toc: --> <li><a href="._week37-bs048.html#the-same-example-but-now-with-cross-validation" style="font-size: 80%;">The same example but now with cross-validation</a></li>
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<!-- navigation toc: --> <li><a href="._week37-bs050.html#linking-the-regression-analysis-with-a-statistical-interpretation" style="font-size: 80%;">Linking the regression analysis with a statistical interpretation</a></li>
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<!-- navigation toc: --> <li><a href="._week37-bs053.html#expectation-value-and-variance-for-boldsymbol-beta" style="font-size: 80%;">Expectation value and variance for \( \boldsymbol{\beta} \)</a></li>
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<h2 id="resampling-methods" class="anchor">Resampling methods </h2>
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<!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<p>Resampling methods are an indispensable tool in modern
statistics. They involve repeatedly drawing samples from a training
set and refitting a model of interest on each sample in order to
obtain additional information about the fitted model. For example, in
order to estimate the variability of a linear regression fit, we can
repeatedly draw different samples from the training data, fit a linear
regression to each new sample, and then examine the extent to which
the resulting fits differ. Such an approach may allow us to obtain
information that would not be available from fitting the model only
once using the original training sample.
</p>
<p>Two resampling methods are often used in Machine Learning analyses,</p>
<ol>
<li> The <b>bootstrap method</b></li>
<li> and <b>Cross-Validation</b></li>
</ol>
<p>In addition there are several other methods such as the Jackknife and the Blocking methods. We will discuss in particular
cross-validation and the bootstrap method.
</p>
</div>
</div>
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