Unterschiede

Hier werden die Unterschiede zwischen zwei Versionen angezeigt.

--- p:ki:seltene_ereignisse [2023/09/29 11:43] – Tscherter, Vincent
+++ p:ki:seltene_ereignisse [2024/05/02 08:57] (aktuell) – Tscherter, Vincent
@@ Zeile 15: / Zeile 15: @@
 💡 Für die folgenden Betrachtungen schauen wir 500 Tage an und gehen davon aus, dass sich 5 Sturmereignisse darin befinden (d.h. im Mittel 1 Sturmereignis alle 100 Tage). Uns interessieren die beiden Klassen "Sturmtag" und "Normaler Tag".
-☹️ Die Klassifikationsrate beschreibt, wie viele Tage richtig klassifiziert werden. Ein Verfahren, dass nie einen Sturm voraussagt, hat in unserem Fall eine Klassifikationsrate von 99%, ist jedoch unbrauchbar. Somit eignet sich die Klassifikationsrate nicht dafür um festzustellen, wie gut seltene Ereignisse vorhergesagt werden.
+🙁 Die Klassifikationsrate beschreibt, wie viele Tage richtig klassifiziert werden. Ein Verfahren, dass nie einen Sturm voraussagt, hat in unserem Fall eine Klassifikationsrate von 99%, ist jedoch unbrauchbar. Somit eignet sich die Klassifikationsrate nicht dafür um festzustellen, wie gut seltene Ereignisse vorhergesagt werden.
 🩺 Wie stelle ich aber fest, ob ein Sturmprognoseverfahren gut oder schlecht ist? Da müssen wir genauer hinschauen. Bewährt hat sich das Zählen der Grössen $A$, $B$, $C$ und $D$ in der nachfolgenden Tabelle. Z.B. ist $A$ die Anzahl Tage, für die ein Sturm vorhergesagt und dann auch beobachtet wurde. Die anderen Grössen werden analog gezählt.
@@ Zeile 61: / Zeile 61: @@
   - Schaue dir die Daten an. Inmitten von 45 grünen Kreisen (beobachtete normale Tage) befinden sich 5 rote Kreuze (beobachtete Stürme). Ein neuronales Netz wird so trainiert, dass es die 5 roten Kreuze erkennen (die Stürme vorhersagen) kann. Alle Eingaben (Pixel in der linken Grafik) für welche eine Sturmprognose ausgegeben wird, werden mit einer roten Hintergrundfarbe versehen. Alle Eingaben, für welche ein normaler Tag prognostiziert wird, werden mit einer grünen Hintergrundfarbe versehen.
   - Lass das Training weiter laufen. Das Training wird erst nach 10'000 Epochen (Durchgängen) beendet (ein Klick in die Grafik und ein Drücken der Taste ''ECS'' oder auch der Taste ''§'' beendet das Training sofort). Erfahrungsgemäss wird (wenn überhaupt) die rote Klasse erst nach ca. 7000 - 9000 Epochen erkannt, was einige Minuten dauern kann. Falls ein roter Bereich in der Grafik entsteht, so wird die seltene Klasse vorhergesagt. Falls bis zum Schluss kein roter Bereich entsteht, so starte das Training erneut. \\ 💡 Alles, was Rot unterlegt wird, wird vom neuronalen Netz als Sturm interpretiert. Wird ein rotes Kreuz rot unterlegt, so wurde das rote Kreuz Sturm korrekt vorhergesagt. Wird ein grüner Kreis rot unterlegt, so wurde für diesen Tag ein Fehlalarm ausgegeben.\\ ⚠️ Studiere während des Trainings die nachfolgende "Erklärung der Ausgabe des Programms".
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 </WRAP>
@@ Zeile 74: / Zeile 74: @@
 == ✍ Auftrag – neuronales Netz auswerten ==
   - Falls noch nicht gemacht, lies die obigen Erklärungen zur Ausgabe des neuronalen Netzes **sorgfältig** durch!
-  - Warte bis das Training beendet ist (und ein roter Bereich eingezeichnet wurde). Halte im Textfeld fest, nach wie vielen Epochen am Ende welcher MSE und welche HIT und FAR für welchen Threshold erzielt wurden.
+  - Warte bis das Training beendet ist (und ein roter Bereich eingezeichnet wurde). Halte im Textfeld fest, nach wie vielen Epochen am Ende welcher MSE und welche HIT und FAR erzielt wurden.\\ 💡 Hier wird immer der Threshold 0.5 für alle Experimente verwendet. In der nächsten Übung wirst du auch mit anderen Thresholds experimentieren.
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 </WRAP>
@@ Zeile 87: / Zeile 87: @@
 == ✍ Auftrag – Grenzen ausloten 1 ==
   - Lasse das nachfolgende Programm (mehrfach) laufen, bis die seltene Klasse erkannt wird.\\ ⚠️ Studiere während des Trainings die nachfolgende "Erklärung der Ausgabe des Programms".
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 </WRAP>
@@ Zeile 120: / Zeile 120: @@
   * **Alternative Loss-Funktionen**: Es werden andere Loss-Funktionen genutzt, welche die seltene Klasse stärker gewichten. Dieses Vorgehen weist ähnliche Vor- und Nachteile auf wie das Resampling.
   * **Alternative Verfahren**: Es wird versucht neue, spezielle Verfahren für ein gegebenes Problem zu entwickeln oder mehrere der hier angeführten Techniken zu kombinieren. Das kann bessere Resultate bringen, weist häufig aber auch die gleichen Nachteile wie das Resampling auf und ist in der Regel sehr aufwändig umzusetzen.
-  * **Aufgeben**: Kommt häufiger vor als man denkt. Das Problem wird dann entweder beschrieben oder manchmal leider auch schlicht ignoriert...
+  * **Aufgeben**: Kommt häufiger vor als man denkt. Das Problem wird dann entweder nur beschrieben oder manchmal leider auch schlicht ignoriert...
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