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Could not fix Python 3.12 · checked 17 Oct 2026 · model cost $0.142

We ran out of time to try more fixes for step s4.

3 changes to make it work today

Run block 1: basic random forest feature importance

s2 · run_code migrate

Replace deprecated load_boston with fetch_california_housing and fix print syntax for Python 3.

@@ -1,13 +1,12 @@
-from sklearn.datasets import load_boston
+from sklearn.datasets import fetch_california_housing
from sklearn.ensemble import RandomForestRegressor
import numpy as np
-#Load boston housing dataset as an example
-boston = load_boston()
-X = boston["data"]
-Y = boston["target"]
-names = boston["feature_names"]
+# Load california housing dataset as an example
+california = fetch_california_housing(as_frame=False)
+X = california["data"]
+Y = california["target"]
+names = california["feature_names"]
rf = RandomForestRegressor()
rf.fit(X, Y)
-print "Features sorted by their score:"
-print sorted(zip(map(lambda x: round(x, 4), rf.feature_importances_), names),
- reverse=True)
+print("Features sorted by their score:")
+print(sorted(zip(map(lambda x: round(x, 4), rf.feature_importances_), names), reverse=True))

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Run block 2: correlated features example

s3 · run_code migrate

Fix print syntax, update numpy random seed call, and convert map to list for Python 3.

@@ -1,4 +1,4 @@
size = 10000
-np.random.seed(seed=10)
+np.random.seed(10)
X_seed = np.random.normal(0, 1, size)
X0 = X_seed + np.random.normal(0, .1, size)
@@ -9,5 +9,4 @@
rf = RandomForestRegressor(n_estimators=20, max_features=2)
-rf.fit(X, Y);
-print "Scores for X0, X1, X2:", map(lambda x:round (x,3),
- rf.feature_importances_)
+rf.fit(X, Y)
+print("Scores for X0, X1, X2:", list(map(lambda x: round(x, 3), rf.feature_importances_)))

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Run block 3: mean decrease accuracy (permutation importance)

s4 · run_code migrate

Update ShuffleSplit import to current location, use california dataset, and fix print syntax.

@@ -1,17 +1,17 @@
-from sklearn.cross_validation import ShuffleSplit
+from sklearn.model_selection import ShuffleSplit
from sklearn.metrics import r2_score
from collections import defaultdict
-X = boston["data"]
-Y = boston["target"]
+X = california["data"]
+Y = california["target"]
rf = RandomForestRegressor()
scores = defaultdict(list)
-#crossvalidate the scores on a number of different random splits of the data
+# crossvalidate the scores on a number of different random splits of the data
for train_idx, test_idx in ShuffleSplit(len(X), 100, .3):
X_train, X_test = X[train_idx], X[test_idx]
Y_train, Y_test = Y[train_idx], Y[test_idx]
- r = rf.fit(X_train, Y_train)
+ rf.fit(X_train, Y_train)
acc = r2_score(Y_test, rf.predict(X_test))
for i in range(X.shape[1]):
@@ -19,6 +19,5 @@
np.random.shuffle(X_t[:, i])
shuff_acc = r2_score(Y_test, rf.predict(X_t))
- scores[names[i]].append((acc-shuff_acc)/acc)
-print "Features sorted by their score:"
-print sorted([(round(np.mean(score), 4), feat) for
- feat, score in scores.items()], reverse=True)
+ scores[names[i]].append((acc - shuff_acc) / acc)
+print("Features sorted by their score:")
+print(sorted([(round(np.mean(score), 4), feat) for feat, score in scores.items()], reverse=True))

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What we tried

SyntaxError: Missing parentheses in call to 'print'. Did you mean print(...)?

3 rounds of fixes.

TypeError: ShuffleSplit.__init__() takes from 1 to 2 positional arguments but 4 were given

1 round of fixes, none passed.

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