- What is a good KS score for logistic regression?
- What is metric in machine learning?
- What is a KS model?
- Can KS be negative?
- What is a good Gini?
- Is Somers d same as Gini?
- What is a good Gini for a scorecard?
- What is Ks in logistic regression?
- How is Gini calculated in Kansas?
- What is a good Ks value?
- What is an example of a metric?
- What does a KS test show?
- What is Overfitting problem?
- What is performance in machine learning?
What is a good KS score for logistic regression?
Important Note – In this case, KS is maximum at third decile and KS score is 59.1.
Ideally, it should be in first three deciles and score lies between 40 and 70.
And there should not be more than 10 points (in absolute) difference between training and validation KS score..
What is metric in machine learning?
Different performance metrics are used to evaluate different Machine Learning Algorithms. … The metrics that you choose to evaluate your machine learning model is very important. Choice of metrics influences how the performance of machine learning algorithms is measured and compared.
What is a KS model?
K-S or Kolmogorov-Smirnov chart measures performance of classification models. More accurately, K-S is a measure of the degree of separation between the positive and negative distributions.
Can KS be negative?
The KS statistic is the maximum vertical distance between the curves and is indicated by the vertical red line. As the reference sample is on the left, the arrow points downwards, so the statistic is negative.
What is a good Gini?
Gini index < 0.2 represents perfect income equality, 0.2–0.3 relative equality, 0.3–0.4 adequate equality, 0.4–0.5 big income gap, and above 0.5 represents severe income gap. Therefore, the warning level of Gini index is 0.4. Cite. 1 Recommendation.
Is Somers d same as Gini?
Summary. Gini coefficient is a summary statistic that can also be known as Accuracy Ratio or Somers’D. … It is recommended to use the Gini Coefficient, since it is more stable, but be aware of both definitions.
What is a good Gini for a scorecard?
A Gini value of 100% means that a characteristic/scorecard distinguishes perfectly. A typical credit scorecard has a Gini coefficient of 40-60%. Behaviour scorecards have values of 70-80%. A very powerful characteristic can have a Gini coefficient of 25%.
What is Ks in logistic regression?
KS Statistic or Kolmogorov-Smirnov statistic is the maximum difference between the cumulative true positive and cumulative false positive rate. It is often used as the deciding metric to judge the efficacy of models in credit scoring.
How is Gini calculated in Kansas?
It measures the area between the cumulative response curve and the 45-degree line. Gini is actually equivalent to the AUC but differing by a scale factor — Gini = 2 * AUC -1.
What is a good Ks value?
K-S should be a high value (Max =1.0) when the fit is good and a low value (Min = 0.0) when the fit is not good. … When the K-S value goes below 0.05, you will be informed that the Lack of fit is significant.
What is an example of a metric?
So, the units for length, weight (mass) and capacity(volume) in the metric system are: Length: Millimeter (mm), Decimeter (dm), Centimeter (cm), Meter (m), and Kilometer (km) are used to measure how long or wide or tall an object is. … Examples include measuring weight of fruits or, our own body weight.
What does a KS test show?
The two sample Kolmogorov-Smirnov test is a nonparametric test that compares the cumulative distributions of two data sets(1,2). … The KS test report the maximum difference between the two cumulative distributions, and calculates a P value from that and the sample sizes.
What is Overfitting problem?
Overfitting is a modeling error in statistics that occurs when a function is too closely aligned to a limited set of data points. … Thus, attempting to make the model conform too closely to slightly inaccurate data can infect the model with substantial errors and reduce its predictive power.
What is performance in machine learning?
Performance evaluation is an important aspect of the machine learning process. … The focus is on the three main subtasks of evaluation: measuring performance, resampling the data, and assessing the statistical significance of the results.