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[Structuring Machine Learning Projects] week1. ML Strategy (1)
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<span class="label label-default">Series</span>
Part 8 of «Andrew Ng Deep Learning MOOC»
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目录
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<div id="toc"><ul><li><a class="toc-href" href="#i-introduction-to-ml-strategy" title="I-Introduction to ML Strategy">I-Introduction to ML Strategy</a><ul><li><a class="toc-href" href="#why-ml-strategy" title="Why ML Strategy">Why ML Strategy</a></li><li><a class="toc-href" href="#orthogonalization" title="Orthogonalization">Orthogonalization</a></li></ul></li><li><a class="toc-href" href="#ii-setting-up-your-goal_1" title="II-Setting up your goal">II-Setting up your goal</a><ul><li><a class="toc-href" href="#single-number-evaluation-metric" title="Single number evaluation metric">Single number evaluation metric</a></li><li><a class="toc-href" href="#satisficing-and-optimizing-metric" title="Satisficing and Optimizing metric">Satisficing and Optimizing metric</a></li><li><a class="toc-href" href="#train-dev-test-distributions" title="Train-dev-test distributions">Train-dev-test distributions</a></li><li><a class="toc-href" href="#size-of-the-dev-and-test-sets" title="Size of the dev and test sets">Size of the dev and test sets</a></li><li><a class="toc-href" href="#when-to-change-devtest-sets-and-metrics" title="When to change dev/test sets and metrics">When to change dev/test sets and metrics</a></li></ul></li><li><a class="toc-href" href="#iii-comparing-to-human-level-performance_1" title="III-Comparing to human-level performance">III-Comparing to human-level performance</a><ul><li><a class="toc-href" href="#why-human-level-performance" title="Why human-level performance?">Why human-level performance?</a></li><li><a class="toc-href" href="#avoidable-bias" title="Avoidable bias">Avoidable bias</a></li><li><a class="toc-href" href="#understanding-human-level-performance" title="Understanding human-level performance">Understanding human-level performance</a></li><li><a class="toc-href" href="#surpassing-human-level-performance" title="Surpassing human-level performance">Surpassing human-level performance</a></li><li><a class="toc-href" href="#improving-your-model-performance" title="Improving your model performance">Improving your model performance</a></li></ul></li></ul></div>
</div>
</div>
<h2 id="i-introduction-to-ml-strategy">I-Introduction to ML Strategy</h2>
<h3 id="why-ml-strategy">Why ML Strategy</h3>
<p>A lot of ideas of improving ML performance: <em>strategy on how to choose</em>.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image.png"/> </p>
<p>→ how to figure out which ones to pursue and which ones to discard ? </p>
<h3 id="orthogonalization">Orthogonalization</h3>
<p>How to tune hyperparams & what to expect. </p>
<p>TV tuning example: each knob does only one thing.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image001.png"/> </p>
<p><strong>Chain of assumptions in ML</strong>:<br/>
training set performance → dev set → test set → real world </p>
<ul>
<li>"one knob for each chain" </li>
<li>Will go through these "knobs" in this course. </li>
<li>Don't use early stopping: this is not orthogonalized enough </li>
</ul>
<p><img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image005.png"/> </p>
<h2 id="ii-setting-up-your-goal_1">II-Setting up your goal</h2>
<h3 id="single-number-evaluation-metric">Single number evaluation metric</h3>
<p>Faster progress if only have <em>one single real number evaluation metric</em>. → more efficient in making decisions. </p>
<p>example:<br/>
Using both percision and recall as metric is not good → <em>difficule to pick the best model to keep on iterating from.</em><br/>
→ Use F1 score instead. </p>
<h3 id="satisficing-and-optimizing-metric">Satisficing and Optimizing metric</h3>
<p>When it's difficult to pick a single real number eval metric → <em>set up satisficing and optimizing metrics.</em> </p>
<p>example: accuracy & running time trade-off<br/>
Instead of doing a linear combination of the two, use this:<br/>
maximize accuracy<br/>
subject to running time <= 100 ms<br/>
In this case: accuracy is <em>optimizing metric</em>, running time is <em>satisficing metric</em>. </p>
<p>In general:<br/>
if having N metrics → <em>pick 1 as optimizing metric, the N-1 rest as satisficing metric</em>. </p>
<p>example: assistant wake-up word accuracy VS false positive.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image006.png"/> </p>
<h3 id="train-dev-test-distributions">Train-dev-test distributions</h3>
<p>How to setup dev/test sets.<br/>
Idea: dev/test sets come from the same distribution. </p>
<p>example: cat classification in different regions.<br/>
This is bad dev/test setup:<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image007.png"/><br/>
Good practice: random shuffle data and split into dev/test sets. </p>
<p><img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image008.png"/> </p>
<h3 id="size-of-the-dev-and-test-sets">Size of the dev and test sets</h3>
<p>Pre-DL era, old way of splitting data: 70/30 or 60/30/10 split.<br/>
→ resonable when datasets are small. (100~10k examples) </p>
<p>In DL era: much more training (~1M) examples.<br/>
⇒ 98/1/1 split is more resonable. </p>
<ul>
<li><strong>Size of test/dev set</strong>: <em>big enough to give high confidence in system's performance</em>. </li>
<li>OK to not have a test set, but not recommended. </li>
</ul>
<h3 id="when-to-change-devtest-sets-and-metrics">When to change dev/test sets and metrics</h3>
<p><strong>example 1</strong><br/>
cat classification: algo A has pornographic false positives.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image009.png"/><br/>
→ change metric to penalize heavily pornographic FPs.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image011.png"/><br/>
to implement this weighting, need to go through dev/test sets. </p>
<p><strong>example 2</strong><br/>
Cat classification: user's upload is blury while trained on high quality images.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image012.png"/> </p>
<h2 id="iii-comparing-to-human-level-performance_1">III-Comparing to human-level performance</h2>
<h3 id="why-human-level-performance">Why human-level performance?</h3>
<p>Workflow ML can be more efficient when trying to match human level performance. </p>
<p>Bayes optional error: best possible error, <em>theoritical optimal</em>. </p>
<p><img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image013.png"/><br/>
ML progress usually slows down after surpassing human-level performance: </p>
<ul>
<li>usually human-level is not far from Bayes optimal </li>
<li>as long as ML performace < human, there are tools to improve performance. </li>
</ul>
<p><img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image014.png"/> </p>
<h3 id="avoidable-bias">Avoidable bias</h3>
<p>Using human rating can prevent overfitting on training set.<br/>
exapmle: compare training set error with human performance. </p>
<ul>
<li>err_train > err_human ⇒ focus on reducing bias (e.g. bigger NN) </li>
<li>err_train ~= err_human ⇒ focus on reducing variance (e.g. regularize, more training data) </li>
</ul>
<p><img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image015.png"/><br/>
→ Use human-level error as a proxy for Bayes error. </p>
<p>Terminology: </p>
<ul>
<li>
<p><strong>Avoidable bias</strong> is the <em>gap</em> between training err and Bayes err.<br/>
(interpretataion: some errors are inavoidable because <em>Bayes err is not 0</em>.) </p>
</li>
<li>
<p><strong>Variance</strong>: gap between training err and dev err. </p>
</li>
</ul>
<h3 id="understanding-human-level-performance">Understanding human-level performance</h3>
<p>"<em>Human level error as proxy for Bayes error</em>" </p>
<p>example: Medical image classification.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image016.png"/><br/>
⇒ Should pick lowest human error as an estimate (upper bound) of Bayes error. </p>
<p>Error analysis example (which human-err to pick to estimate avoidable bias):<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image018.png"/> </p>
<h3 id="surpassing-human-level-performance">Surpassing human-level performance</h3>
<p>What's the avoidable bias when err_train and err_dev are smaller than err_human ?<br/>
→ less clear in choosing directions. </p>
<p>examples of tasks where ML >> human performance:<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image019.png"/><br/>
⇒ all these tasks are: </p>
<ul>
<li>learned from structured data </li>
<li>are not natural perception tasks </li>
<li>have processed huge amount of data </li>
</ul>
<h3 id="improving-your-model-performance">Improving your model performance</h3>
<p>Recall: two fundamental assumptions of supervised learning: </p>
<ul>
<li>You can fit training set well (achieve to ~= avoidable bias) </li>
<li>The performance on training set generalize well to dev/test sets. (achieve low variance) </li>
</ul>
<p>The big roadmap:<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c3wk1/pasted_image020.png"/> </p>
</div>
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<div id="toc"><ul><li><a class="toc-href" href="#i-introduction-to-ml-strategy" title="I-Introduction to ML Strategy">I-Introduction to ML Strategy</a><ul><li><a class="toc-href" href="#why-ml-strategy" title="Why ML Strategy">Why ML Strategy</a></li><li><a class="toc-href" href="#orthogonalization" title="Orthogonalization">Orthogonalization</a></li></ul></li><li><a class="toc-href" href="#ii-setting-up-your-goal_1" title="II-Setting up your goal">II-Setting up your goal</a><ul><li><a class="toc-href" href="#single-number-evaluation-metric" title="Single number evaluation metric">Single number evaluation metric</a></li><li><a class="toc-href" href="#satisficing-and-optimizing-metric" title="Satisficing and Optimizing metric">Satisficing and Optimizing metric</a></li><li><a class="toc-href" href="#train-dev-test-distributions" title="Train-dev-test distributions">Train-dev-test distributions</a></li><li><a class="toc-href" href="#size-of-the-dev-and-test-sets" title="Size of the dev and test sets">Size of the dev and test sets</a></li><li><a class="toc-href" href="#when-to-change-devtest-sets-and-metrics" title="When to change dev/test sets and metrics">When to change dev/test sets and metrics</a></li></ul></li><li><a class="toc-href" href="#iii-comparing-to-human-level-performance_1" title="III-Comparing to human-level performance">III-Comparing to human-level performance</a><ul><li><a class="toc-href" href="#why-human-level-performance" title="Why human-level performance?">Why human-level performance?</a></li><li><a class="toc-href" href="#avoidable-bias" title="Avoidable bias">Avoidable bias</a></li><li><a class="toc-href" href="#understanding-human-level-performance" title="Understanding human-level performance">Understanding human-level performance</a></li><li><a class="toc-href" href="#surpassing-human-level-performance" title="Surpassing human-level performance">Surpassing human-level performance</a></li><li><a class="toc-href" href="#improving-your-model-performance" title="Improving your model performance">Improving your model performance</a></li></ul></li></ul></div>
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