Model Evaluation 편집하기

'''Model Evaluation''' refers to the process of assessing the performance of a machine learning model on a given dataset. It is a critical step in machine learning workflows to ensure that the model generalizes well to unseen data and performs as expected for the target application.
==Objectives of Model Evaluation==
The key objectives of model evaluation are:
*'''Assess Performance:''' Measure how well the model predicts outcomes.
*'''Compare Models:''' Evaluate multiple models to select the best-performing one.
*'''Detect Overfitting/Underfitting:''' Ensure the model generalizes well without fitting too closely to the training data.
*'''Optimize Parameters:''' Identify areas for model improvement.
==Types of Evaluation Metrics==
Model evaluation metrics vary depending on the type of machine learning problem:
===Classification Metrics===
*'''Accuracy:''' Proportion of correct predictions out of total predictions.
*'''Precision:''' Proportion of true positives among predicted positives.
*'''Recall (Sensitivity):''' Proportion of true positives among actual positives.
*'''F1 Score:''' Harmonic mean of precision and recall.
*'''ROC-AUC:''' Measures the area under the Receiver Operating Characteristic curve, balancing true positive and false positive rates.
===Regression Metrics===
*'''Mean Absolute Error (MAE):''' Average of absolute differences between actual and predicted values.
*'''Mean Squared Error (MSE):''' Average of squared differences between actual and predicted values.
*'''Root Mean Squared Error (RMSE):''' Square root of MSE, providing error in the same units as the output.
*'''R² (Coefficient of Determination):''' Proportion of variance explained by the model.
===Clustering Metrics===
*'''Silhouette Score:''' Measures how well clusters are separated and cohesive.
*'''Adjusted Rand Index (ARI):''' Compares clustering results with ground truth.
*'''Calinski-Harabasz Index:''' Evaluates cluster density and separation.
==Model Evaluation Techniques==
Several techniques are used to evaluate models effectively:
===Holdout Method===
*Split the dataset into training, validation, and testing sets.
*Train the model on the training set, tune hyperparameters on the validation set, and evaluate performance on the testing set.
===Cross-Validation===
*Partition the dataset into \( k \) folds and perform \( k \)-fold cross-validation.
*Each fold serves as a testing set once, and the remaining \( k-1 \) folds are used for training.
===Bootstrapping===
*Randomly resample the dataset with replacement and evaluate the model on each resampled set.
===Leave-One-Out Cross-Validation (LOOCV)===
*Use all but one data point for training and test on the single data point. Repeat for every data point.
==Example: Evaluating a Classification Model in Python==
Using scikit-learn to evaluate a classification model:<syntaxhighlight lang="python">
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# Example dataset
X = [[1, 2], [2, 3], [3, 4], [4, 5]]
y = [0, 0, 1, 1]

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

# Train model
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)

# Predictions
y_pred = model.predict(X_test)

# Evaluate
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Precision:", precision_score(y_test, y_pred))
print("Recall:", recall_score(y_test, y_pred))
print("F1 Score:", f1_score(y_test, y_pred))
</syntaxhighlight>
==Applications of Model Evaluation==
*'''Healthcare:''' Assessing the performance of diagnostic models.
*'''Finance:''' Evaluating risk prediction models for credit scoring.
*'''Marketing:''' Measuring the effectiveness of customer segmentation models.
*'''Natural Language Processing (NLP):''' Testing sentiment analysis or text classification models.
==Advantages==
*'''Ensures Reliability:''' Provides confidence that the model will perform well on unseen data.
*'''Identifies Weaknesses:''' Highlights areas where the model struggles, enabling targeted improvements.
*'''Supports Model Selection:''' Helps choose the best model for a specific problem.
==Limitations==
*'''Computational Cost:''' Some evaluation techniques, like cross-validation, can be time-consuming.
*'''Data Dependency:''' Results may vary depending on the dataset split or sampling method.
*'''Over-reliance on Metrics:''' Metrics may not fully capture real-world performance.
==Related Concepts and See Also==
*[[Cross-Validation]]
*[[Hyperparameter Tuning]]
*[[Overfitting]]
*[[Underfitting]]
*[[Confusion Matrix]]
*[[Bias and Variance]]
*[[Clustering Metrics]]
[[분류:Data Science]]