Float
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
Float has 1 fact recorded in Dontopedia across 1 reference.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (150)
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.
rdf:typeRdf:type(123)
- 0.01
ex:0.01 - 0.1
ex:0.1 - 0.1
ex:0.1 - 0.1
ex:0.1 - 0.6
ex:0.6 - 0.8
ex:0.8 - 0.8
ex:0.8 - 0.9
ex:0.9 - 0.91
ex:0.91 - Accuracy
ex:accuracy - Accuracy
ex:accuracy - Accuracy
ex:accuracy - Accuracy Float
ex:accuracy-float - Accuracy Value
ex:accuracy-value - Angle
ex:angle - Ap
ex:ap - Average Bleu Score
ex:average-bleu-score - Average Precision
ex:average_precision - Average Ratio
ex:average_ratio - Average Recall
ex:average_recall - Average Response Time
ex:average_response_time - Average Response Time
ex:average_response_time - Average Response Time Variable
ex:average_response_time-variable - Best Precision
ex:best_precision - Best Precision
ex:best_precision - Best Score
ex:best_score - Best Threshold
ex:best_threshold - Cache Hit Rate
ex:cache_hit_rate - Clarity Improvement
ex:clarity_improvement - Clarity Percentage
ex:clarity_percentage - Clarity Score
ex:clarity_score - Completion Percentage
ex:completion_percentage - Complexity
ex:complexity - Complexity
ex:complexity - Complexity
ex:complexity - Complexity
ex:complexity - Complexity
ex:complexity - Complexity
ex:complexity - Complexity Value
ex:complexity-value - Complexity Value
ex:complexity_value - Compliance Rate
ex:compliance_rate - Contextual Similarity Value
ex:contextual-similarity-value - Coverage Goal
ex:coverage_goal - Coverage Goal Example
ex:coverage_goal_example - Cpu Percent
ex:cpu_percent - Current Timestamp
ex:current_timestamp - Current Usage
ex:current_usage - Default Value
ex:default_value - Dense Score
ex:dense_score - Duration
ex:duration - Duration
ex:duration - Duration Float
ex:duration-float - End Time
ex:end_time - End Time Variable
ex:end_time-variable - Error Rate
ex:error_rate - Error Rate
ex:error_rate - Error Rate Float
ex:error-rate-float - Error Rate Variable
ex:error-rate-variable - Example Error Rate
ex:example_error_rate - Execution Time
ex:execution-time - Execution Time
ex:execution_time - Execution Time
ex:execution_time - Latency
ex:latency - Latency
ex:latency - Latency
ex:latency - Map Value
ex:map_value - Median Response Time
ex:median_response_time - Min Distance
ex:min-distance - Min Distance
ex:min_distance - Min Distance
ex:min_distance - Mismatch
ex:mismatch - Numeric Value
ex:numeric-value - Overall Completion Rate
ex:overall_completion_rate - P90 Response Time
ex:p90_response_time - Param1
ex:param1 - Param1 Factor
ex:param1_factor - Param2
ex:param2 - Param2 Factor
ex:param2_factor - Parameter Value 1.2
ex:parameter-value-1.2 - Parameter Value 2.2
ex:parameter-value-2.2 - Percentage Improvement
ex:percentage-improvement - Percentage Literal
ex:percentage_literal - Point Eight
ex:pointEight - Precision
ex:precision - Precision
ex:precision - Precision
ex:precision - Precision
ex:precision - Precision
ex:precision - Precision
ex:precision - Precision Updated
ex:precision_updated - Processing Time
ex:processing-time - Process Time
ex:process_time - Pruning Amount
ex:pruning-amount - Recall
ex:recall - Recall
ex:recall - Refinement Rate
ex:refinement_rate - Required Completion Rate
ex:required_completion_rate - Response Time Variable
ex:response_time-variable - Risk Score
ex:risk_score - Score
ex:score - Score
ex:score - Sentiment
ex:sentiment - Similarity Score
ex:similarity_score - Similarity Threshold
ex:similarity_threshold - Simulated Processing Time
ex:simulated-processing-time - Single Clarity Value
ex:single-clarity-value - Sparse Score
ex:sparse_score - Start Time
ex:start_time - Start Time
ex:start_time - Start Time Variable
ex:start_time-variable - Test Size
ex:test_size - Threshold
ex:threshold - Threshold
ex:threshold - Threshold
ex:threshold - Threshold07
ex:threshold07 - Threshold Value
ex:threshold_value - Total Weight
ex:total_weight - Trend
ex:trend - Tuple Element
ex:tuple-element - Uptime Increment
ex:uptime-increment - Uptime Initial
ex:uptime-initial - Weighted Param1 Factor
ex:weighted_param1_factor - Weighted Param2 Factor
ex:weighted_param2_factor
hasTypeHas Type(6)
- Error Rate
ex:error_rate - Failure Detection Target
ex:failure_detection_target - Failure Detection Target Parameter
ex:failure-detection-target-parameter - Impact Attribute
ex:impact-attribute - Likelihood Attribute
ex:likelihood-attribute - Target Completion Parameter
ex:target-completion-parameter
elementTypeElement Type(3)
- Enhanced Latencies
ex:enhanced_latencies - Measured Complexities
ex:measured_complexities - Thresholds
ex:thresholds
elementTypesElement Types(2)
- Tuple
ex:Tuple - Vector List
ex:vector_list
attributeTypeAttribute Type(1)
- Est
ex:est
dataCategoryData Category(1)
- Price Field
ex:price-field
dataFormatData Format(1)
- Initial Skill Level
ex:initial-skill-level
dataTypeData Type(1)
- Relevance Score
ex:relevance-score
ex:hasConfidenceEx:has Confidence(1)
- Fact
ex:Fact
expectedTypeExpected Type(1)
- Relevance Score
ex:relevance-score
hasDataTypeHas Data Type(1)
- Elapsed Time
ex:elapsed_time
hasElementTypeHas Element Type(1)
- Vector Property
ex:vectorProperty
hasValueTypeHas Value Type(1)
- Scores
ex:scores
importedItemImported Item(1)
- Float Import
ex:float-import
importsImports(1)
- Sql Alchemy Imports
SQLAlchemy_imports
keyTypeKey Type(1)
- Precision Results
ex:precision_results
parameterTypeParameter Type(1)
- Add Sub Task Method
ex:add-sub-task-method
returnsTypeReturns Type(1)
- Evaluate Model
ex:evaluate_model
secondReturnTypeSecond Return Type(1)
- Function Return Type
ex:function-return-type
valueTypeValue Type(1)
- Precision Results
ex:precision_results
Other facts (1)
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Timeline
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References (1)
ctx:claims/beam/1680fd31-ef75-4b8f-b41d-f9807171b358- full textbeam-chunktext/plain1 KB
doc:beam/1680fd31-ef75-4b8f-b41d-f9807171b358Show excerpt
grid_search.fit(X_train_tfidf, y_train) # Best model best_model = grid_search.best_estimator_ # Make predictions predictions = best_model.predict(X_test_tfidf) # Calculate the recall score recall = recall_score(y_test, predictions) print…
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