GridSearchCV-Eingaben

Teste dein Wissen über die Eingaben von GridSeachCV, indem du die folgende Frage beantwortest.

In der Konsole stehen drei GridSearchCV-Objekte zur Verfügung: model_1, model_2, model_3. Beachte, dass keine Daten zum Fitten dieser Modelle vorhanden sind. Stattdessen sollst du anhand ihrer Konstruktion antworten.

Welches dieser GridSearchCV-Objekte würde nicht funktionieren, wenn wir versuchen, es zu fitten?

Diese Übung ist Teil des Kurses

<Kurs>Hyperparameter-Tuning in Python</Kurs>

Interaktive praktische Übung

Verwandle Theorie mit einer unserer interaktiven Übungen in die Praxis

Diese Übung ist Teil des Kurses

<Kurs>Hyperparameter-Tuning in Python</Kurs>

Mittlere SchwierigkeitSchwierigkeitsgrad

4.9+

Kurs kostenlos starten

In this introductory chapter you will learn the difference between hyperparameters and parameters. You will practice extracting and analyzing parameters, setting hyperparameter values for several popular machine learning algorithms. Along the way you will learn some best practice tips & tricks for choosing which hyperparameters to tune and what values to set & build learning curves to analyze your hyperparameter choices.

Exercise 1: Introduction & 'Parameters'Exercise 2: Parameters in Logistic Regression Exercise 3: Extracting a Logistic Regression parameter Exercise 4: Extracting a Random Forest parameter Exercise 5: Introducing Hyperparameters Exercise 6: Hyperparameters in Random Forests Exercise 7: Exploring Random Forest Hyperparameters Exercise 8: Hyperparameters of KNN Exercise 9: Setting & Analyzing Hyperparameter Values Exercise 10: Automating Hyperparameter Choice Exercise 11: Building Learning Curves

This chapter introduces you to a popular automated hyperparameter tuning methodology called Grid Search. You will learn what it is, how it works and practice undertaking a Grid Search using Scikit Learn. You will then learn how to analyze the output of a Grid Search & gain practical experience doing this.

Exercise 1: Einführung in Grid Search Exercise 2: Grid-Search-Funktionen erstellen Exercise 3: Mehrere Hyperparameter iterativ abstimmen Exercise 4: Wie viele Modelle?Exercise 5: Grid Search mit Scikit-Learn Exercise 6: GridSearchCV-Eingaben

Aktuelle Übung

Exercise 7: GridSearchCV mit Scikit Learn Exercise 8: Ein Grid-Search-Output verstehen Exercise 9: Die besten Ergebnisse nutzen Exercise 10: Die Grid-Search-Ergebnisse erkunden Exercise 11: Die besten Ergebnisse analysieren Exercise 12: Die besten Ergebnisse nutzen

In this chapter you will be introduced to another popular automated hyperparameter tuning methodology called Random Search. You will learn what it is, how it works and importantly how it differs from grid search. You will learn some advantages and disadvantages of this method and when to choose this method compared to Grid Search. You will practice undertaking a Random Search with Scikit Learn as well as visualizing & interpreting the output.

Exercise 1: Introducing Random Search Exercise 2: Randomly Sample Hyperparameters Exercise 3: Randomly Search with Random Forest Exercise 4: Visualizing a Random Search Exercise 5: Random Search in Scikit Learn Exercise 6: RandomSearchCV inputs Exercise 7: The RandomizedSearchCV Object Exercise 8: RandomSearchCV in Scikit Learn Exercise 9: Comparing Grid and Random Search Exercise 10: Comparing Random & Grid Search Exercise 11: Grid and Random Search Side by Side

In this final chapter you will be given a taste of more advanced hyperparameter tuning methodologies known as ''informed search''. This includes a methodology known as Coarse To Fine as well as Bayesian & Genetic hyperparameter tuning algorithms. You will learn how informed search differs from uninformed search and gain practical skills with each of the mentioned methodologies, comparing and contrasting them as you go.

Exercise 1: Informed Search: Coarse to Fine Exercise 2: Visualizing Coarse to Fine Exercise 3: Coarse to Fine Iterations Exercise 4: Informed Search: Bayesian Statistics Exercise 5: Bayes Rule in Python Exercise 6: Bayesian Hyperparameter tuning with Hyperopt Exercise 7: Informed Search: Genetic Algorithms Exercise 8: Genetic Hyperparameter Tuning with TPOT Exercise 9: Analysing TPOT's stability Exercise 10: Congratulations!