Spaces:
Running
Running
| <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> | |