Model Training and Evaluation
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Model Training and Evaluation are crucial steps in Machine Learning that ensure a model learns correctly from data and performs well on unseen data.
1. Model Training
Model training is the process where a machine learning algorithm learns patterns from training data by adjusting its parameters.
Steps in Model Training:
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Select a suitable algorithm
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Provide training data
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Optimize model parameters
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Minimize errors using a loss function
Goal:
To build a model that can make accurate predictions.
2. Model Evaluation
Model evaluation measures how well the trained model performs on new or unseen data.
Why Evaluation is Important:
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Prevents overfitting
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Checks real-world performance
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Helps compare different models
Common Evaluation Metrics
For Classification Models:
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Accuracy
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Precision
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Recall
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F1-Score
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Confusion Matrix
For Regression Models:
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Mean Absolute Error (MAE)
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Mean Squared Error (MSE)
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Root Mean Squared Error (RMSE)
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R² Score
Train-Test Split
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Data is divided into training data and testing data
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Common split: 80% training – 20% testing
Cross-Validation
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Data is split into multiple parts (folds)
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Model is trained and tested multiple times
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Improves reliability of results
Conclusion
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Training helps the model learn from data
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Evaluation ensures the model performs accurately and reliably
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Both steps are essential for building a successful machine learning system
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