param_grid
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
param_grid has 34 facts recorded in Dontopedia across 6 references, with 5 live disagreements.
34 facts·11 predicates·6 sources·5 in dispute
Mostly:has key(10), has parameter(6), rdf:type(4)
Maturity scale
raw canonical shape-checked rule-derived certifiedHas Keyin disputehasKey
- C[4]sourceall time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
- penalty[4]sourceall time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
- solver[4]sourceall time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
- n_estimators[4]all time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
- max_depth[4]all time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
- learning_rate[4]all time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
- kernel[4]all time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
- gamma[4]all time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
- min_samples_split[4]all time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
- alpha[4]all time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
Inbound mentions (8)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
configured-withConfigured With(1)
- Grid Search Cv
ex:grid-search-cv
definesDefines(1)
- Parameter Grid Definition
ex:parameter-grid-definition
hasElementHas Element(1)
- Model Tuple
ex:model-tuple
hasParameterHas Parameter(1)
- Grid Search
ex:grid-search
hasVariableHas Variable(1)
- Code Snippet
ex:code-snippet
requiresRequires(1)
- Grid Search Cv
ex:grid-search-cv
searchesOverSearches Over(1)
- Grid Search Execution
ex:grid-search-execution
usesUses(1)
- Grid Search Execution
ex:grid-search-execution
Other facts (23)
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.
23 facts
| Predicate | Value | Ref |
|---|---|---|
| Has Parameter | N Neighbors | [2] |
| Has Parameter | Weights | [2] |
| Has Parameter | Metric | [2] |
| Has Parameter | classifier__n_estimators | [6] |
| Has Parameter | classifier__max_depth | [6] |
| Has Parameter | classifier__min_samples_split | [6] |
| Rdf:type | Dictionary | [1] |
| Rdf:type | Parameter Grid | [2] |
| Rdf:type | Parameter Grid | [3] |
| Rdf:type | Dictionary | [4] |
| Contains | Weights Parameter | [1] |
| Contains | C Values | [3] |
| Contains | Penalty Values | [3] |
| Contains | Solver Values | [3] |
| Specifies | C Regularization | [3] |
| Specifies | Penalty Type | [3] |
| Specifies | Solver Algorithm | [3] |
| Structure | Dictionary With Weights Key | [1] |
| Dictionary Type | Python Dict | [2] |
| Combinations of | Hyperparameter Space | [3] |
| Is Dictionary | true | [4] |
| Associated With | Model | [5] |
| Required by | Grid Search Cv | [5] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
—
typebeam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb
ex:Dictionary
—
containsbeam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb
ex:weights-parameter
—
structurebeam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb
ex:dictionary-with-weights-key
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typebeam/9e5c3595-3f3d-4a73-a70b-a74beec8b366
ex:ParameterGrid
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hasParameterbeam/9e5c3595-3f3d-4a73-a70b-a74beec8b366
ex:n-neighbors
—
hasParameterbeam/9e5c3595-3f3d-4a73-a70b-a74beec8b366
ex:weights
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hasParameterbeam/9e5c3595-3f3d-4a73-a70b-a74beec8b366
ex:metric
—
dictionaryTypebeam/9e5c3595-3f3d-4a73-a70b-a74beec8b366
ex:PythonDict
—
typebeam/f23ba10e-5767-47e9-84b0-112f567f31bc
ex:ParameterGrid
—
labelbeam/f23ba10e-5767-47e9-84b0-112f567f31bc
param_grid
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containsbeam/f23ba10e-5767-47e9-84b0-112f567f31bc
ex:C-values
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containsbeam/f23ba10e-5767-47e9-84b0-112f567f31bc
ex:penalty-values
—
containsbeam/f23ba10e-5767-47e9-84b0-112f567f31bc
ex:solver-values
—
specifiesbeam/f23ba10e-5767-47e9-84b0-112f567f31bc
ex:C-regularization
—
specifiesbeam/f23ba10e-5767-47e9-84b0-112f567f31bc
ex:penalty-type
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specifiesbeam/f23ba10e-5767-47e9-84b0-112f567f31bc
ex:solver-algorithm
—
combinationsOfbeam/f23ba10e-5767-47e9-84b0-112f567f31bc
ex:hyperparameter-space
—
typebeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
ex:Dictionary
—
isDictionarybeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
true
—
hasKeybeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
C
—
hasKeybeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
penalty
—
hasKeybeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
solver
—
hasKeybeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
n_estimators
—
hasKeybeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
max_depth
—
hasKeybeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
learning_rate
—
hasKeybeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
kernel
—
hasKeybeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
gamma
—
hasKeybeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
min_samples_split
—
hasKeybeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
alpha
—
associated-withbeam/7835e578-f2e3-46a0-aa40-4497812bf8de
ex:model
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required-bybeam/7835e578-f2e3-46a0-aa40-4497812bf8de
ex:grid-search-cv
—
hasParameterbeam/894e4fae-39aa-43e2-8e08-00a71ba66883
classifier__n_estimators
—
hasParameterbeam/894e4fae-39aa-43e2-8e08-00a71ba66883
classifier__max_depth
—
hasParameterbeam/894e4fae-39aa-43e2-8e08-00a71ba66883
classifier__min_samples_split
References (6)
6 references
ctx:claims/beam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb- full textbeam-chunktext/plain1 KB
doc:beam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbbShow excerpt
#### 2. Normalization Normalize the scores to ensure they are on the same scale. #### 3. Advanced Fusion Techniques Consider using a weighted sum with normalization. ### Example Code ```python import numpy as np from sklearn.model_select…
ctx:claims/beam/9e5c3595-3f3d-4a73-a70b-a74beec8b366ctx:claims/beam/f23ba10e-5767-47e9-84b0-112f567f31bcctx: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/7835e578-f2e3-46a0-aa40-4497812bf8de- full textbeam-chunktext/plain1 KB
doc:beam/7835e578-f2e3-46a0-aa40-4497812bf8deShow excerpt
recall = recall_score(y_test, predictions) print(f'{name} Recall score: {recall:.3f}') print(classification_report(y_test, predictions)) print(confusion_matrix(y_test, predictions)) print('-' * 50) ``` ### Explanat…
ctx:claims/beam/894e4fae-39aa-43e2-8e08-00a71ba66883- full textbeam-chunktext/plain1 KB
doc:beam/894e4fae-39aa-43e2-8e08-00a71ba66883Show excerpt
X = np.random.rand(11000, 10) y = np.random.randint(0, 2, size=11000) # Split data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Define pipeline pipeline = Pipeline([ ('scaler', StandardSc…
See also
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