Practical Data Science using Python - Logistic Regression - Model Evaluation - AUC-ROC

The video tutorial covers logistic regression using stats model and scikit-learn, highlighting differences such as regularization and intercept handling. It explains model training, evaluation, and the use of confusion matrix to assess performance. Various classification metrics like sensitivity and specificity are discussed, along with the ROC curve and AUC for evaluating binary outcomes.

What is a key difference between logistic regression in statsmodels and scikit-learn?

• Scikit-learn includes an intercept by default.

• Scikit-learn does not support logistic regression.

• Statsmodels uses L1 regularization by default.

• Statsmodels requires manual feature scaling.

What is the default regularization used by scikit-learn's logistic regression?

• L1 regularization

• No regularization

• Elastic Net

• L2 regularization

What is the purpose of using the predict function in logistic regression?

• To visualize the data

• To predict values from the dataset

• To evaluate model accuracy

• To train the model

What does a confusion matrix help to determine?

• The number of features in the dataset

• The accuracy of the model

• The distribution of predicted and actual values

• The best regularization technique

In a confusion matrix, what does a false positive indicate?

• An actual negative predicted as negative

• An actual negative predicted as positive

• An actual positive predicted as negative

• An actual positive predicted as positive

What does sensitivity measure in classification metrics?

• The overall accuracy of the model

• The precision of the model

• The ability to correctly predict non-churn cases

• The ability to correctly predict churn cases

What is specificity in the context of classification metrics?

• The rate of false positives

• The rate of true positives

• The ability to correctly predict non-churn cases

• The overall accuracy of the model

What does the ROC curve plot?

• True positive rate vs. false positive rate

• Accuracy vs. precision

• True negatives vs. false negatives

• Sensitivity vs. specificity

What does a higher AUC value indicate about a model?

• The model has a higher false positive rate

• The model has a lower true positive rate

• The model is more accurate

• The model is less accurate

What is the significance of the area under the ROC curve (AUC)?

• It shows the model's precision

• It reflects the model's sensitivity

• It indicates the model's ability to distinguish between classes

• It measures the model's accuracy