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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<title>PyScript Test</title>
<link rel="stylesheet" href="https://pyscript.net/alpha/pyscript.css" />
<script defer src="https://pyscript.net/alpha/pyscript.js"></script>
<py-env>
- scikit-learn
- tabulate
</py-env>
<!-- from https://stackoverflow.com/a/62032824 -->
<link rel="stylesheet"
href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.6.0/styles/default.min.css">
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.6.0/highlight.min.js"
integrity="sha512-gU7kztaQEl7SHJyraPfZLQCNnrKdaQi5ndOyt4L4UPL/FHDd/uB9Je6KDARIqwnNNE27hnqoWLBq+Kpe4iHfeQ=="
crossorigin="anonymous"
referrerpolicy="no-referrer"></script>
<script>hljs.initHighlightingOnLoad();</script>
</head>
<body>
<p>Define your own sklearn classifier and evaluate it on the toy dataset. An example is shown below:</p>
<pre>
<code class="python">from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(random_state=0)
evaluate(clf)</code>
</pre>
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>.
<br><br>
Enter your code below, then press Shift+Enter:
<py-script>
from statistics import mean
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_validate
import tabulate
X, y = make_classification(n_samples=1000, n_informative=10, random_state=0)
def evaluate(clf):
cv_result = cross_validate(clf, X, y, scoring='accuracy', cv=5)
time_fit = sum(cv_result['fit_time'])
time_score = sum(cv_result['score_time'])
print(f"Mean test accuracy: {mean(cv_result['test_score']):.3f}")
print(f"Total training time: {time_fit:.1f} seconds")
print(f"Total time for scoring: {time_score:.1f} seconds")
show_result = {'split': [1, 2, 3, 4, 5], 'accuracy': cv_result['test_score']}
print("Accuracy for each cross validation split:")
return tabulate.tabulate(show_result, tablefmt='html', headers='keys', floatfmt='.3')
</py-script>
<py-repl auto-generate="true"></py-repl>
</body>
</html>
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