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Jun 12, 2024
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Set random state when splitting data to update scikit-learn #762

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57 changes: 29 additions & 28 deletions packages/scikit-learn/index.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -570,7 +570,8 @@ One good method to keep in mind is Gaussian Naive Bayes
>>> from sklearn.model_selection import train_test_split

>>> # split the data into training and validation sets
>>> X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target)
>>> X_train, X_test, y_train, y_test = train_test_split(
... digits.data, digits.target, random_state=42)

>>> # train the model
>>> clf = GaussianNB()
Expand All @@ -581,9 +582,9 @@ One good method to keep in mind is Gaussian Naive Bayes
>>> predicted = clf.predict(X_test)
>>> expected = y_test
>>> print(predicted)
[5 1 7 2 8 9 4 3 9 3 6 2 3 2 6 7 4 3 5 7 5 7 0 1 2 5 9 8 1 8...]
[6 9 3 7 2 2 5 8 5 2 1 1 7 0 4 8 3 7 8 8 4 3 9 7 5 6 3 5 6 3...]
>>> print(expected)
[5 8 7 2 8 9 4 3 7 3 6 2 3 2 6 7 4 3 5 7 5 7 0 1 2 5 3 3 1 8...]
[6 9 3 7 2 1 5 2 5 2 1 9 4 0 4 2 3 7 8 8 4 3 9 7 5 6 3 5 6 3...]

As above, we plot the digits with the predicted labels to get an idea of
how well the classification is working.
Expand All @@ -607,11 +608,11 @@ the number of matches::

>>> matches = (predicted == expected)
>>> print(matches.sum())
371
385
>>> print(len(matches))
450
>>> matches.sum() / float(len(matches))
0.82444...
0.8555...

We see that more than 80% of the 450 predictions match the input. But
there are other more sophisticated metrics that can be used to judge the
Expand All @@ -625,20 +626,20 @@ combines several measures and prints a table with the results::
>>> print(metrics.classification_report(expected, predicted))
precision recall f1-score support
<BLANKLINE>
0 1.00 0.98 0.99 45
1 0.91 0.66 0.76 44
2 0.91 0.56 0.69 36
3 0.89 0.67 0.77 49
4 0.95 0.83 0.88 46
5 0.93 0.93 0.93 45
6 0.92 0.98 0.95 47
7 0.75 0.96 0.84 50
8 0.49 0.97 0.66 39
9 0.85 0.67 0.75 49
0 1.00 0.95 0.98 43
1 0.85 0.78 0.82 37
2 0.85 0.61 0.71 38
3 0.97 0.83 0.89 46
4 0.98 0.84 0.90 55
5 0.90 0.95 0.93 59
6 0.90 0.96 0.92 45
7 0.71 0.98 0.82 41
8 0.60 0.89 0.72 38
9 0.90 0.73 0.80 48
<BLANKLINE>
accuracy 0.82 450
macro avg 0.86 0.82 0.82 450
weighted avg 0.86 0.82 0.83 450
accuracy 0.86 450
macro avg 0.87 0.85 0.85 450
weighted avg 0.88 0.86 0.86 450
<BLANKLINE>


Expand All @@ -647,16 +648,16 @@ is a *confusion matrix*: it helps us visualize which labels are being
interchanged in the classification errors::

>>> print(metrics.confusion_matrix(expected, predicted))
[[44 0 0 0 0 0 0 0 0 1]
[ 0 29 0 0 0 0 1 6 6 2]
[ 0 1 20 1 0 0 0 0 14 0]
[ 0 0 0 33 0 2 0 1 11 2]
[ 0 0 0 0 38 1 2 4 1 0]
[ 0 0 0 0 0 42 1 0 2 0]
[ 0 0 0 0 0 0 46 0 1 0]
[ 0 0 0 0 1 0 0 48 0 1]
[ 0 1 0 0 0 0 0 0 38 0]
[ 0 1 2 3 1 0 0 5 4 33]]
[[41 0 0 0 0 1 0 1 0 0]
[ 0 29 2 0 0 0 0 0 4 2]
[ 0 2 23 0 0 0 1 0 12 0]
[ 0 0 1 38 0 1 0 0 5 1]
[ 0 0 0 0 46 0 2 7 0 0]
[ 0 0 0 0 0 56 1 1 0 1]
[ 0 0 0 0 1 1 43 0 0 0]
[ 0 0 0 0 0 1 0 40 0 0]
[ 0 2 0 0 0 0 0 2 34 0]
[ 0 1 1 1 0 2 1 5 2 35]]

We see here that in particular, the numbers 1, 2, 3, and 9 are often
being labeled 8.
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2 changes: 1 addition & 1 deletion requirements.txt
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Expand Up @@ -4,7 +4,7 @@ matplotlib==3.9.0
pandas==2.2.2
patsy==0.5.6
pyarrow==16.1.0
scikit-learn==1.4.2
scikit-learn==1.5.0
scikit-image==0.23.2
sympy==1.12.1
statsmodels==0.14.2
Expand Down