What is a cap curve?

What is a cap curve?

A cumulative accuracy profile (CAP) is a concept utilized in data science to visualize discrimination power. The CAP of a model represents the cumulative number of positive outcomes along the y-axis versus the corresponding cumulative number of a classifying parameter along the x-axis. The output is called a CAP curve.

What does a lift curve compare?

The lift curve uses the ratio between percentage of clickers to the percentage of customer contacted. Here the highest lift equal to 2.1 means that for the first 10\% of your “best” customers you will reach 2.1 more clickers than if you were contacting random customers.

What is ROC curve in machine learning?

An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters: True Positive Rate. False Positive Rate.

What is ROC curve used for?

ROC curves are frequently used to show in a graphical way the connection/trade-off between clinical sensitivity and specificity for every possible cut-off for a test or a combination of tests. In addition the area under the ROC curve gives an idea about the benefit of using the test(s) in question.

What does ROC curve tell you?

The ROC curve shows the trade-off between sensitivity (or TPR) and specificity (1 – FPR). Classifiers that give curves closer to the top-left corner indicate a better performance. The closer the curve comes to the 45-degree diagonal of the ROC space, the less accurate the test.

What is capping in machine learning?

“Capped means that the median house value is set so that their values does not go beyond a certain limit, so your machine learning algorithm may learn that the price never go beyond that set limit, and it certainly can be a problem in future stages of the model.

What does a lift curve show?

Measures the performance of a chosen classifier against a random classifier. The Lift curve shows the curves for analysing the proportion of true positive data instances in relation to the classifier’s threshold or the number of instances that we classify as positive.

What are lift charts?

A lift chart graphically represents the improvement that a mining model provides when compared against a random guess, and measures the change in terms of a lift score. By comparing the lift scores for different models, you can determine which model is best.

What is the ROC curve in machine learning?

Receiver Operating Characteristic (ROC) Curve The Receiver Operating Characteristic Curve, better known as the ROC Curve, is an excellent method for measuring the performance of a Classification model. The True Positive Rate (TPR) is plot against False Positive Rate (FPR) for the probabilities of the classifier predictions.

What is the difference between a CAP curve and ROC curve?

ROC curve and CAP curve(Gains chart) are mostly used how good your classification model. Main difference is how the both the curves are interpreting values in X and Y axis. Gains vs ROC curves.

What is the ROC curve of a classifier?

ROC curve, also known as Receiver Operating Characteristics Curve, is a metric used to measure the performance of a classifier model. The ROC curve depicts the rate of true positives with respect to the rate of false positives, therefore highlighting the sensitivity of the classifier model.

What is CAP curve in machine learning?

A CAP (Cumulative Accuracy Profile) curve is a performance measurement for classification problems. It is used to evaluate a model by comparing the current curve to both the ‘ perfect ’ or ‘ ideal ’ curve and a ‘ randomized ’ curve. A decent or good model will have a CAP that is in the middle of the perfect and random curves.