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Benjamin Bossan
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Parent(s):
2e1d656
Users can test their own classifiers
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index.html
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<html lang="en">
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<head>
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<meta charset="utf-8" />
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<link rel="stylesheet" href="https://pyscript.net/alpha/pyscript.css" />
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<script defer src="https://pyscript.net/alpha/pyscript.js"></script>
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</head>
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<body>
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</body>
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</html>
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<html lang="en">
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<head>
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<meta charset="utf-8" />
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<title>PyScript Test</title>
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<link rel="stylesheet" href="https://pyscript.net/alpha/pyscript.css" />
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<script defer src="https://pyscript.net/alpha/pyscript.js"></script>
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<py-env>
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- scikit-learn
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- tabulate
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</py-env>
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<!-- from https://stackoverflow.com/a/62032824 -->
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<link rel="stylesheet"
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href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.6.0/styles/default.min.css">
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<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.6.0/highlight.min.js"
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integrity="sha512-gU7kztaQEl7SHJyraPfZLQCNnrKdaQi5ndOyt4L4UPL/FHDd/uB9Je6KDARIqwnNNE27hnqoWLBq+Kpe4iHfeQ=="
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crossorigin="anonymous"
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referrerpolicy="no-referrer"></script>
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<script>hljs.initHighlightingOnLoad();</script>
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</head>
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<body>
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<p>Define your own sklearn classifier and evaluate it on the toy dataset. An example is shown below:</p>
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<pre>
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<code class="python">from sklearn.linear_model import LogisticRegression
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clf = LogisticRegression(random_state=0)
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evaluate(clf)</code>
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</pre>
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Try to achieve a test accuracy of 0.85 or better! Get some inspiration for possible classifiers <a href="https://scikit-learn.org/stable/supervised_learning.html" title="List of sklearn estimators">here</a>.
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<br><br>
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Enter your code below, then press Shift+Enter:
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<py-script>
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from statistics import mean
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from sklearn.datasets import make_classification
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from sklearn.model_selection import cross_validate
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import tabulate
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X, y = make_classification(n_samples=1000, n_informative=10, random_state=0)
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def evaluate(clf):
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cv_result = cross_validate(clf, X, y, scoring='accuracy', cv=5)
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show_result = {'split': [1, 2, 3, 4, 5], 'accuracy': cv_result['test_score']}
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print(f"Mean test accuracy: {mean(cv_result['test_score']):.3f}")
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return tabulate.tabulate(show_result, tablefmt='html', headers='keys', floatfmt='.3')
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</py-script>
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<py-repl auto-generate="false"></py-repl>
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</body>
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</html>
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