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

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

Assessment

Interactive Video

Computers

10th - 12th Grade

Practice Problem

Hard

Created by

Wayground Content

FREE Resource

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.

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10 questions

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1.

OPEN ENDED QUESTION

3 mins • 1 pt

What is the main purpose of using logistic regression in the context of the provided text?

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2.

OPEN ENDED QUESTION

3 mins • 1 pt

Explain the key differences between logit and logistic regression as mentioned in the text.

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3.

OPEN ENDED QUESTION

3 mins • 1 pt

What role does L2 regularization play in preventing overfitting in logistic regression?

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4.

OPEN ENDED QUESTION

3 mins • 1 pt

How does the stats model logic function differ from the circuit learned logistic regression in terms of intercept inclusion?

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5.

OPEN ENDED QUESTION

3 mins • 1 pt

What does the accuracy score indicate about the model's performance, and why can it be misleading?

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6.

OPEN ENDED QUESTION

3 mins • 1 pt

What is the significance of the confusion matrix in evaluating the performance of a logistic regression model?

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7.

OPEN ENDED QUESTION

3 mins • 1 pt

Define true positives, false positives, true negatives, and false negatives in the context of the confusion matrix.

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