### Answer

The statement "The shape of ROC curves are the leading indicator of an overfitted logistic regression" is **False**.

### Explanation

1. **ROC Curve Basics**: The Receiver Operating Characteristic (ROC) curve is a graphical representation used to evaluate the performance of a classification model at different threshold settings. It plots the True Positive Rate (TPR) against the False Positive Rate (FPR).

2. **Overfitting**: Overfitting occurs when a model learns the training data too well, capturing noise as if it were a true trend. It leads to poor generalization to unseen data.

3. **Identifying Overfitting**: While ROC curves can indicate how well a model performs, they are not definitive indicators of overfitting. A model might show a good ROC curve on the training data but still be overfitted.

4. **Evaluation Measures**: To determine overfitting, one should compare model performance on training versus validation/test datasets. Significant discrepancies might indicate overfitting, whereas a consistent performance across datasets suggests better generalization.

In summary, while the shape of the ROC curves provides valuable insights into model performance, they do not directly indicate whether a logistic regression model is overfitted.

Question

### Answer

The statement "The shape of ROC curves are the leading indicator of an overfitted logistic regression" is **False**.

### Explanation

1. **ROC Curve Basics**: The Receiver Operating Characteristic (ROC) curve is a graphical representation used to evaluate the performance of a classification model at different threshold settings. It plots the True Positive Rate (TPR) against the False Positive Rate (FPR).

2. **Overfitting**: Overfitting occurs when a model learns the training data too well, capturing noise as if it were a true trend. It leads to poor generalization to unseen data.

3. **Identifying Overfitting**: While ROC curves can indicate how well a model performs, they are not definitive indicators of overfitting. A model might show a good ROC curve on the training data but still be overfitted.

4. **Evaluation Measures**: To determine overfitting, one should compare model performance on training versus validation/test datasets. Significant discrepancies might indicate overfitting, whereas a consistent performance across datasets suggests better generalization.

In summary, while the shape of the ROC curves provides valuable insights into model performance, they do not directly indicate whether a logistic regression model is overfitted.

Knowee AI · Accepted Answer

### Answer

The statement "The shape of ROC curves are the leading indicator of an overfitted logistic regression" is **False**.

### Explanation

1. **ROC Curve Basics**: The Receiver Operating Characteristic (ROC) curve is a graphical representation used to evaluate the performance of a classification model at different threshold settings. It plots the True Positive Rate (TPR) against the False Positive Rate (FPR).

2. **Overfitting**: Overfitting occurs when a model learns the training data too well, capturing noise as if it were a true trend. It leads to poor generalization to unseen data.

3. **Identifying Overfitting**: While ROC curves can indicate how well a model performs, they are not definitive indicators of overfitting. A model might show a good ROC curve on the training data but still be overfitted.

4. **Evaluation Measures**: To determine overfitting, one should compare model performance on training versus validation/test datasets. Significant discrepancies might indicate overfitting, whereas a consistent performance across datasets suggests better generalization.

In summary, while the shape of the ROC curves provides valuable insights into model performance, they do not directly indicate whether a logistic regression model is overfitted.

(True/False) The shape of ROC curves are the leading indicator of an overfitted logistic regression.1 pointTrueFalse

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