Set random state when splitting data (#762)

Author: thomasjpfan

packages/scikit-learn/index.rst

@@ -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()
@@ -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.
@@ -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
@@ -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>
 
 
@@ -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.

requirements.txt

@@ -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