test_size
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
test_size has 14 facts recorded in Dontopedia across 6 references, with 2 live disagreements.
Mostly:has value(5), rdf:type(4), represents(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (3)
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.
usesParameterUses Parameter(2)
- Dataset Split
ex:dataset-split - Data Splitting
ex:data-splitting
hasParameterHas Parameter(1)
- Training Testing Split
ex:training-testing-split
Other facts (12)
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 Value | 0.2 | [1] |
| Has Value | 0.2 | [2] |
| Has Value | 0.2 | [3] |
| Has Value | 0.2 | [4] |
| Has Value | 0.2 | [6] |
| Rdf:type | Parameter | [1] |
| Rdf:type | Float Parameter | [3] |
| Rdf:type | Proportion Parameter | [5] |
| Rdf:type | Hyperparameter | [6] |
| Represents | Testing Set Proportion | [2] |
| Represents | 20-percent-of-data | [5] |
| Has Percentage | 20 | [2] |
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.
References (6)
ctx:claims/beam/e040e300-3af9-406d-923e-f84685e7f8ef- full textbeam-chunktext/plain1 KB
doc:beam/e040e300-3af9-406d-923e-f84685e7f8efShow excerpt
Here's an example of how you might set up the grid search and logging: ```python from sklearn.model_selection import train_test_split from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score import logging # Exa…
ctx:claims/beam/cd20f999-1387-4a3e-9486-0da4fc043940- full textbeam-chunktext/plain1 KB
doc:beam/cd20f999-1387-4a3e-9486-0da4fc043940Show excerpt
2. **Advanced Hyperparameter Tuning**: Allocate 3-4 hours. 3. **Full Integration of Evaluation Metrics**: Allocate 2-3 hours. 4. **Complete Integration with Existing Systems**: Allocate 3-4 hours. 5. **Comprehensive Error Handling and Loggi…
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/46068d53-96d3-4709-a18e-0c4041019936- full textbeam-chunktext/plain1 KB
doc:beam/46068d53-96d3-4709-a18e-0c4041019936Show excerpt
### Step 2: Modify the Code to Use BM25 Here's an example of how you can integrate BM25 into your proof of concept: ```python import pandas as pd from sklearn.model_selection import train_test_split from sklearn.metrics import recall_scor…
ctx:claims/beam/28d34bc8-0c0d-4b85-aae9-2f70febdb3e1- full textbeam-chunktext/plain1 KB
doc:beam/28d34bc8-0c0d-4b85-aae9-2f70febdb3e1Show excerpt
```python import numpy as np from sklearn.metrics import accuracy_score from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split import redis import logging # Set up logging configuration log…
ctx:claims/beam/d375d85b-650d-469e-9f0b-11950f22f89a
See also
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