Orange: Lift Curve - Revision history

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Referensi

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Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/evaluate/liftcurve.html Measures the performance of a chosen classifier against a random classifier. Inputs..."

2020-01-16T07:28:46Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/evaluate/liftcurve.html Measures the performance of a chosen classifier against a random classifier. Inputs..."

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Sumber: https://docs.biolab.si//3/visual-programming/widgets/evaluate/liftcurve.html

Measures the performance of a chosen classifier against a random classifier.

Inputs

Evaluation Results: results of testing classification algorithms

The Lift curve shows the relation between the number of instances which were predicted positive and those that are indeed positive and thus measures the performance of a chosen classifier against a random classifier. The graph is constructed with the cumulative number of cases (in descending order of probability) on the x-axis and the cumulative number of true positives on the y-axis. Lift curve is often used in segmenting the population, e.g., plotting the number of responding customers against the number of all customers contacted. You can also determine the optimal classifier and its threshold from the graph.

../../_images/LiftCurve-stamped.png

Choose the desired Target class. The default class is chosen alphabetically.

If test results contain more than one classifier, the user can choose which curves she or he wants to see plotted. Click on a classifier to select or deselect the curve.

Show lift convex hull plots a convex hull over lift curves for all classifiers (yellow curve). The curve shows the optimal classifier (or combination thereof) for each desired TP/P rate.

Press Save Image if you want to save the created image to your computer in a .svg or .png format.

Produce a report.

2-D pane with P rate (population) as x-axis and TP rate (true positives) as a y-axis. The diagonal line represents the behavior of a random classifier. Click and drag to move the pane and scroll in or out to zoom. Click on the “A” sign at the bottom left corner to realign the pane.

Note! The perfect classifier would have a steep slope towards 1 until all classes are guessed correctly and then run straight along 1 on y-axis to (1,1).
Example

At the moment, the only widget which gives the right type of the signal needed by the Lift Curve is Test & Score.

In the example below, we try to see the prediction quality for the class ‘survived’ on the Titanic dataset. We compared three different classifiers in the Test Learners widget and sent them to Lift Curve to see their performance against a random model. We see the Tree classifier is the best out of the three, since it best aligns with lift convex hull. We also see that its performance is the best for the first 30% of the population (in order of descending probability), which we can set as the threshold for optimal classification.

../../_images/LiftCurve-example.png
References

Handouts of the University of Notre Dame on Data Mining - Lift Curve. Available here.

==Referensi==

* https://docs.biolab.si//3/visual-programming/widgets/evaluate/liftcurve.html

==Pranala Menarik==

* [[Orange]]

@@ Line 26: / Line 26: @@
 Pada saat ini, satu-satu-nya widget yang memberikan tipe sinyal yang benar untuk widget Lift Curve adalah widget Test & Score.
-Pada contoh di bawah ini, kita ingin melihat kualitas prediksi untuk class ‘survived’ pada dataset Titanic. Kita membandingkan tiga classifer yang berbeda di widget Test & Score dan mengirimkan hasilnya ke widget Lift Curve untuk melihat performance mereka terhadap model random. Kita dapat melihat dengan jelas bahwa Tree Classifer yang terbaik diantara ke tiga-nya karena dia yang menghasilkan curva yang paling menempel ke curva hasil lift curve.
+Pada contoh di bawah ini, kita ingin melihat kualitas prediksi untuk class ‘survived’ pada dataset Titanic. Kita membandingkan tiga classifer yang berbeda di widget Test & Score dan mengirimkan hasilnya ke widget Lift Curve untuk melihat performance mereka terhadap model random. Kita dapat melihat dengan jelas bahwa Tree Classifer yang terbaik diantara ke tiga-nya karena dia yang menghasilkan curva yang paling menempel ke curva hasil lift curve. Kita juga melihat bahwa kinerja terbaiknya terjadi pada 30% awal populasi (dalam urutan probabilitas menurun), yang dapat kita tetapkan sebagai ambang batas untuk klasifikasi optimal.
 [[File:LiftCurve-example.png|center|600px|thumb]]

@@ Line 26: / Line 26: @@
 Pada saat ini, satu-satu-nya widget yang memberikan tipe sinyal yang benar untuk widget Lift Curve adalah widget Test & Score.
-Pada contoh di bawah ini, kita ingin melihat kualitas prediksi untuk class ‘survived’ pada dataset Titanic. Kita membandingkan tiga classifer yang berbeda di widget test Learner dan m
+Pada contoh di bawah ini, kita ingin melihat kualitas prediksi untuk class ‘survived’ pada dataset Titanic. Kita membandingkan tiga classifer yang berbeda di widget Test & Score dan mengirimkan hasilnya ke widget Lift Curve untuk melihat performance mereka terhadap model random. Kita dapat melihat dengan jelas bahwa Tree Classifer yang terbaik diantara ke tiga-nya karena dia yang menghasilkan curva yang paling menempel ke curva hasil lift curve.
-We compared three different classifiers in the Test Learners widget and sent them to Lift Curve to see their performance against a random model. We see the Tree classifier is the best out of the three, since it best aligns with lift convex hull. We also see that its performance is the best for the first 30% of the population (in order of descending probability), which we can set as the threshold for optimal classification.
+to see their performance against a random model. We see the Tree classifier is the best out of the three, since it best aligns with lift convex hull. Kita juga melihat bahwa kinerja terbaiknya terjadi pada 30% awal populasi (dalam urutan probabilitas menurun), yang dapat kita tetapkan sebagai ambang batas untuk klasifikasi optimal.
 [[File:LiftCurve-example.png|center|600px|thumb]]

@@ Line 24: / Line 24: @@
 ==Contoh==
-At the moment, the only widget which gives the right type of the signal needed by the Lift Curve is Test & Score.
+Pada saat ini, satu-satu-nya widget yang memberikan tipe sinyal yang benar untuk widget Lift Curve adalah widget Test & Score.
-In the example below, we try to see the prediction quality for the class ‘survived’ on the Titanic dataset. We compared three different classifiers in the Test Learners widget and sent them to Lift Curve to see their performance against a random model. We see the Tree classifier is the best out of the three, since it best aligns with lift convex hull. We also see that its performance is the best for the first 30% of the population (in order of descending probability), which we can set as the threshold for optimal classification.
+Pada contoh di bawah ini, kita ingin melihat kualitas prediksi untuk class ‘survived’ pada dataset Titanic. Kita membandingkan tiga classifer yang berbeda di widget test Learner dan m
-[[File:LiftCurve-example.png|center|200px|thumb]]
+We compared three different classifiers in the Test Learners widget and sent them to Lift Curve to see their performance against a random model. We see the Tree classifier is the best out of the three, since it best aligns with lift convex hull. We also see that its performance is the best for the first 30% of the population (in order of descending probability), which we can set as the threshold for optimal classification.
 ==Referensi==

@@ Line 11: / Line 11: @@
 Widget Lift curve menunjukkan hubungan antara jumlah instance yang diprediksi positif dan yang memang positif dan dengan demikian mengukur kinerja classifier yang dipilih terhadap classifier random. Graph dibangun dengan jumlah kasus kumulatif (dalam urutan probabilitas menurun) pada sumbu x dan jumlah kumulatif true positif pada sumbu y. Widget Lift Curve sering digunakan dalam pengelompokan populasi, misalnya, memplot jumlah pelanggan yang merespons terhadap jumlah semua pelanggan yang dihubungi. Kita juga dapat menentukan optimal classifier dan threshold-nya dari graph.
-[[File:LiftCurve-stamped.png|center|200px|thumb]]
+[[File:LiftCurve-stamped.png|center|600px|thumb]]
 * Choose the desired Target class. The default class is chosen alphabetically.

@@ Line 3: / Line 3: @@
-Mengukur performance sebuah classifier yang dipilih terhadap random classifier.
+Widget Lift Curve Mengukur performance sebuah classifier yang dipilih terhadap random classifier.
 ==Input==
@@ Line 9: / Line 9: @@
   Evaluation Results: results of testing classification algorithms
-Lift curve menunjukkan hubungan antara jumlah instance yang diprediksi positif dan yang memang positif dan dengan demikian mengukur kinerja classifier yang dipilih terhadap classifier random. Graph dibangun dengan jumlah kasus kumulatif (dalam urutan probabilitas menurun) pada sumbu x dan jumlah kumulatif true positif pada sumbu y. Lift Curve sering digunakan dalam pengelompokan populasi, misalnya, memplot jumlah pelanggan yang merespons terhadap jumlah semua pelanggan yang dihubungi. Kita juga dapat menentukan optimal classifier dan threshold-nya dari graph.
+Widget Lift curve menunjukkan hubungan antara jumlah instance yang diprediksi positif dan yang memang positif dan dengan demikian mengukur kinerja classifier yang dipilih terhadap classifier random. Graph dibangun dengan jumlah kasus kumulatif (dalam urutan probabilitas menurun) pada sumbu x dan jumlah kumulatif true positif pada sumbu y. Widget Lift Curve sering digunakan dalam pengelompokan populasi, misalnya, memplot jumlah pelanggan yang merespons terhadap jumlah semua pelanggan yang dihubungi. Kita juga dapat menentukan optimal classifier dan threshold-nya dari graph.
 [[File:LiftCurve-stamped.png|center|200px|thumb]]

@@ Line 5: / Line 5: @@
 Measures the performance of a chosen classifier against a random classifier.
-Inputs
+==Input==
-    Evaluation Results: results of testing classification algorithms
+ Evaluation Results: results of testing classification algorithms
 The Lift curve shows the relation between the number of instances which were predicted positive and those that are indeed positive and thus measures the performance of a chosen classifier against a random classifier. The graph is constructed with the cumulative number of cases (in descending order of probability) on the x-axis and the cumulative number of true positives on the y-axis. Lift curve is often used in segmenting the population, e.g., plotting the number of responding customers against the number of all customers contacted. You can also determine the optimal classifier and its threshold from the graph.
@@ Line 13: / Line 13: @@
 [[File:LiftCurve-stamped.png|center|200px|thumb]]
-    Choose the desired Target class. The default class is chosen alphabetically.
+* Choose the desired Target class. The default class is chosen alphabetically.
-    If test results contain more than one classifier, the user can choose which curves she or he wants to see plotted. Click on a classifier to select or deselect the curve.
+* If test results contain more than one classifier, the user can choose which curves she or he wants to see plotted. Click on a classifier to select or deselect the curve.
-    Show lift convex hull plots a convex hull over lift curves for all classifiers (yellow curve). The curve shows the optimal classifier (or combination thereof) for each desired TP/P rate.
+* Show lift convex hull plots a convex hull over lift curves for all classifiers (yellow curve). The curve shows the optimal classifier (or combination thereof) for each desired TP/P rate.
-    Press Save Image if you want to save the created image to your computer in a .svg or .png format.
+* Press Save Image if you want to save the created image to your computer in a .svg or .png format.
-    Produce a report.
+* Produce a report.
--D pane with P rate (population) as x-axis and TP rate (true positives) as a y-axis. The diagonal line represents the behavior of a random classifier. Click and drag to move the pane and scroll in or out to zoom. Click on the “A” sign at the bottom left corner to realign the pane.
+* 2-D pane with P rate (population) as x-axis and TP rate (true positives) as a y-axis. The diagonal line represents the behavior of a random classifier. Click and drag to move the pane and scroll in or out to zoom. Click on the “A” sign at the bottom left corner to realign the pane.
 Note! The perfect classifier would have a steep slope towards 1 until all classes are guessed correctly and then run straight along 1 on y-axis to (1,1).

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	Handouts of the University of Notre Dame on Data Mining - Lift Curve. Available here.		Handouts of the University of Notre Dame on Data Mining - Lift Curve. Available here.
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	==Referensi==		==Referensi==