DecisionTreeClassifier
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
DecisionTreeClassifier has 14 facts recorded in Dontopedia across 2 references, with 4 live disagreements.
Mostly:has parameter max depth(3), has parameter min samples split(3), parameter max depth(3)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (3)
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containsContains(1)
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containsModelContains Model(1)
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providesClassProvides Class(1)
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Other facts (13)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Has Parameter Max Depth | 10 | [1] |
| Has Parameter Max Depth | 20 | [1] |
| Has Parameter Max Depth | 30 | [1] |
| Has Parameter Min Samples Split | 2 | [1] |
| Has Parameter Min Samples Split | 5 | [1] |
| Has Parameter Min Samples Split | 10 | [1] |
| Parameter Max Depth | None | [2] |
| Parameter Max Depth | 10 | [2] |
| Parameter Max Depth | 30 | [2] |
| Rdf:type | Decision Tree Classifier | [1] |
| Rdf:type | Classification Model | [2] |
| Class Name | DecisionTreeClassifier | [2] |
| Parameter Min Samples Split | 2 | [2] |
Timeline
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References (2)
ctx:claims/beam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a- full textbeam-chunktext/plain1 KB
doc:beam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0aShow excerpt
df = pd.read_csv('data.csv') # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(df['text'], df['label'], test_size=0.2, random_state=_42) # Feature extraction vectorizer = TfidfVectorizer()…
ctx:claims/beam/b3aa5dac-a3f5-477c-922c-cef12e6cc5a9- full textbeam-chunktext/plain1 KB
doc:beam/b3aa5dac-a3f5-477c-922c-cef12e6cc5a9Show excerpt
X_train, X_test, y_train, y_test = train_test_split(df['text'], df['label'], test_size=0.2, random_state=42) # Feature extraction vectorizer = TfidfVectorizer() X_train_tfidf = vectorizer.fit_transform(X_train) X_test_tfidf = vectorizer.tr…
See also
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