Split data into training and validation sets
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Split data into training and validation sets has 4 facts recorded in Dontopedia across 3 references.
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| Rdf:type | Code Comment | [1] |
| Rdf:type | Code Comment | [3] |
| Describes | Data Splitting | [2] |
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ctx:claims/beam/2739fb08-c4fc-4bb6-b143-e05bc2133eae- full textbeam-chunktext/plain1 KB
doc:beam/2739fb08-c4fc-4bb6-b143-e05bc2133eaeShow excerpt
```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error class MyMod…
ctx:claims/beam/424105bf-6157-4437-85d8-d148da0857d2- full textbeam-chunktext/plain1 KB
doc:beam/424105bf-6157-4437-85d8-d148da0857d2Show excerpt
X = data.drop(columns=['relevance_score']) y = data['relevance_score'] # Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Define preprocessing steps prep…
ctx:claims/beam/d12b2d61-e885-4664-a34c-5efbe1a9589c- full textbeam-chunktext/plain1 KB
doc:beam/d12b2d61-e885-4664-a34c-5efbe1a9589cShow excerpt
inputs = data['input'] outputs = data['output'] # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(inputs, outputs, test_size=0.2) # Train the pipeline on the training data pipeline.fit(X_t…
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