Logistic Regressionword

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Arizona State University *

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600

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Industrial Engineering

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Apr 3, 2024

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docx

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4

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Logistic Regression 1. There's a potential for class imbalance as there appears to be a significant disparity in the number of observations between the "No" group and the "Yes" category. 2. To gauge the degree of class imbalance, we utilize the class imbalance ratio, which is calculated by dividing the number of samples in the majority class by the number of samples in the minority class. In this case, the class imbalance ratio is 7.8, implying that the "No" class is roughly 7.8 times more prevalent than the "Yes" class. The magnitude of the class imbalance is 36202 / 4639= 7.8 In an ideal scenario, a balanced dataset would have an imbalance ratio nearing 1, indicating a near-even distribution of samples across classes. However, the optimal extent of class imbalance varies based on factors such as the domain and characteristics of the dataset. 3. The second partition = 8169 rows of data. 4. The module responsible for building the logistic regression model in KNIME is crucial for training and evaluating the model.In KNIME, the Logistic Regression Learner module is responsible for building logistic regression models, which are widely used for binary classification tasks. 5. Common solvers for logistic regression include 'lbfgs' and 'liblinear', known for their effectiveness in different scenarios. The Stochastic Average Gradient solver utilizes a modified version of stochastic gradient descent, known for its accelerated convergence compared to traditional stochastic gradient descent methods. It particularly excels in scenarios involving large datasets and tables with a higher number of columns than rows. 6. The default column labels for actual and predicted values are essential for result interpretation and comparison. The default column labels for “actual” values are “subsribe_binary” and “predicted” value is “Prediction(subsribe_binary)” 7. The validation recall accuracy in logistic regression is 0.342 for the "Yes" class and 0.977 for the "No" class. Validation Recall accuracy provides insights into how well
the model predicts the 'No' and 'Yes' classes, which is crucial for evaluating model performance. Since the 'No' class is exceeding the 'Yes' class, the Validation Recall for the 'No' class is likely to be higher. Recall Accuracy Statistics for Validation data T The proportion of actual positive cases, denoted as "Yes" instances, correctly identified by the model is termed recall, also known as sensitivity or true positive rate. A recall accuracy of 0.977 for the "No" class indicates that the model accurately recognized 97.7% of actual "No" instances as "No." Conversely, a recall accuracy of 0.342 for the "Yes" class indicates that the model accurately labeled only 34.2% of actual "Yes" instances as "Yes." This implies a lower accuracy in detecting positive cases by the model. In summary, while the model exhibits high performance in identifying instances of the "No" class, its performance in detecting instances of the "Yes" class is relatively poor. To enhance overall predictive accuracy, further investigation into the factors influencing the model's performance for each class may be necessary. 8. Train versus Validation accuracy Comparing the Train and Validation accuracy helps assess the model's generalization and identify potential overfitting. Validation accuracy: No: 0.977, 0.92, 0.948 Yes: 0.342, 0.654, 0.449 Accuracy: 0.905 Test accuracy: No: 0.976, 0.921, 0.948
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