Holdout (Data Science) 편집하기

'''Holdout''' in data science refers to a method used to evaluate the performance of machine learning models by splitting the dataset into separate parts, typically a training set and a testing set. The testing set, often called the "holdout set," is kept aside during model training and is only used for final evaluation to ensure unbiased performance metrics.
==How Holdout Works==
The holdout method involves the following steps:
*The dataset is split into two (or sometimes three) subsets:
**'''Training Set:''' Used to train the model.
**'''Testing Set (Holdout Set):''' Used to evaluate the model's performance on unseen data.
**(Optional) '''Validation Set:''' Used for hyperparameter tuning and intermediate evaluation.
*The model is trained on the training set and evaluated on the holdout set to measure its generalization capability.
Example:<syntaxhighlight lang="python">
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris

# Load dataset
data = load_iris()
X, y = data.data, data.target

# Split data into training and holdout sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Evaluate on holdout set
accuracy = model.score(X_test, y_test)
print(f"Accuracy on holdout set: {accuracy:.2f}")
</syntaxhighlight>
==Advantages of Holdout==
*'''Simplicity:''' Easy to implement and understand.
*'''Speed:''' Requires training the model only once, making it faster than cross-validation.
*'''Good for Large Datasets:''' When the dataset is sufficiently large, a holdout set can provide a reliable estimate of model performance.
==Limitations of Holdout==
*'''Variance:''' The performance metric depends on the specific train-test split and may vary if the split changes.
*'''Underutilization of Data:''' Only part of the dataset is used for training, which can reduce model accuracy, especially with small datasets.
*'''Bias:''' A single holdout split may not represent the overall data distribution accurately.
==Comparison with Cross-Validation==
Holdout is often compared with cross-validation, another model evaluation technique:
{| class="wikitable"
!Feature!!Holdout!!Cross-Validation
|-
|Simplicity||Simple to implement||More complex
|-
|Computational Cost||Lower||Higher
|-
|Variance||High (depends on the split)||Low (averaged over multiple splits)
|-
|Use of Data||Partial||Utilizes the entire dataset
|}
==Best Practices==
To mitigate the limitations of the holdout method:
*Perform multiple holdout splits (e.g., using random seeds) and average the results to reduce variance.
*Use stratified splitting to ensure class balance in the train and test sets for classification problems.
*For small datasets, prefer cross-validation over holdout for a more reliable estimate of performance.
==Related Concepts and See Also==
*[[Cross-Validation]]
*[[Train-Test Split]]
*[[Validation Set]]
*[[Overfitting]]
*[[Model Evaluation Metrics]]
*[[Bias and Variance]]
*[[Hyperparameter Tuning]]
*[[Data Splitting]]
*[[Generalization in Machine Learning]]

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[[분류:Data Science]]