Test
Logit Regression Test - Machine Learning
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Preguntas del test (17)
1Logit uses a reference line as relation between dependent & independents
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2An Exp(B) value (eB) of 0.80 for a significant variable means the dependent variable will increase an 80% with one unit change on the independent variable (holding the other fixed)
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3Specificity is something we would want it low in our model
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4ROC Curve helps to balance errors from a Logit Regression model
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5Stepwise method will select the best variables for the analysis, although some of them will be non-significant
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6If we wanted to be more restrictive on our model, we would move to the left in the ROC Curve
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7Sensitivity is True Positive rate
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8Every dot in the ROC curve is a different model
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9Logit Regression minimum sample size is approx. 20 to 25 degrees of freedom
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10False Positives are 20
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11Precision is greater than Recall
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12Specificity is 40/50
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13False Positive rate is 20/50
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14Sensitivity is 30
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15Accuracy is 70%
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16If we decrease the threshold that produced this table, the value 10 would decrease
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17If we increase the threshold that produced this table, the value 60 would decrease
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Información
- Descripción
- Informática Test sobre Logit Regression Test - Machine Learning, creado por Angel Intriago el 12/12/2021.
- Autor
- Angel Intriago
- Idioma
- English
- Visitas
- 6
- Etiquetas
- Valoración
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