12. Model Evaluation and Validation

Sections

Navigate through the learning materials and practice exercises.

12

Model Evaluation And Validation Techniques

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This section discusses the importance of model evaluation and validation...
12.1

Importance Of Model Evaluation

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Evaluating machine learning models is essential for ensuring their...
12.2

Common Evaluation Metrics

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This section discusses common evaluation metrics for classification and...
12.2.A

Classification Metrics

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This section covers common classification metrics used to evaluate the...
12.2.B

Regression Metrics

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This section explores key metrics used to evaluate regression models,...
12.3

Data Splitting Techniques

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Data splitting techniques are essential strategies used in machine learning...
12.3.A

Hold-Out Validation

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Hold-out validation is a technique used in model evaluation that separates...
12.3.B

K-Fold Cross-Validation

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K-Fold Cross-Validation is a technique that enhances model validation by...
12.3.C

Stratified K-Fold Cross-Validation

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Stratified K-Fold Cross-Validation is a technique that ensures each fold of...
12.3.D

Leave-One-Out Cross-Validation (Loocv)

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Leave-One-Out Cross-Validation (LOOCV) is a technique for model validation...
12.3.E

Nested Cross-Validation

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Nested cross-validation is a model evaluation technique that separates data...
12.4

Common Pitfalls In Model Evaluation

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This section outlines common mistakes in model evaluation that can lead to...
12.4.A

Overfitting

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Overfitting occurs when a machine learning model performs well on training...
12.4.B

Underfitting

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Underfitting occurs when a model is too simple to capture the underlying...
12.4.C

Data Leakage

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Data leakage refers to the unintentional use of information from the test...
12.4.D

Imbalanced Datasets

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Imbalanced datasets present challenges in model evaluation, as accuracy can...
12.5

Advanced Evaluation Techniques

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This section discusses advanced techniques for evaluating machine learning...
12.5.A

Bootstrapping

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Bootstrapping is a statistical method involving sampling with replacement to...
12.5.B

Time-Series Cross-Validation

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Time-series cross-validation ensures that no future data leaks into the...
12.5.C

Confusion Matrix

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The confusion matrix is a vital tool in evaluating the performance of...
12.5.D

Roc And Precision-Recall Curves

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ROC and Precision-Recall curves are key tools in model evaluation,...
12.6

Hyperparameter Tuning With Evaluation

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Hyperparameter tuning is crucial for optimizing model performance,...
12.7

Best Practices

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Best practices for model evaluation guide data scientists in ensuring the...