Hyperparameter in Random Forests

Wie du gesehen hast, gibt es in einem Random-Forest-Modell mit Scikit-Learn viele verschiedene Hyperparameter. Hier kannst du dir noch einmal in Erinnerung rufen, wie man zwischen einem Hyperparameter und einem Parameter unterscheidet, und schnell prüfen, ob etwas ein Hyperparameter ist.

Welcher der folgenden Punkte ist ein Hyperparameter für das Scikit-Learn-Random-Forest-Modell?

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

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Verwandle Theorie mit einer unserer interaktiven Übungen in die Praxis

Diese Übung ist Teil des Kurses

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

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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: Einführung & „Parameter“Exercise 2: Parameter in der logistischen Regression Exercise 3: Einen Parameter der logistischen Regression extrahieren Exercise 4: Einen Parameter eines Random-Forest extrahieren Exercise 5: Einführung in Hyperparameter Exercise 6: Hyperparameter in Random Forests

Aktuelle Übung

Exercise 7: Random-Forest-Hyperparameter erkunden Exercise 8: Hyperparameter von KNN Exercise 9: Hyperparameterwerte festlegen und analysieren Exercise 10: Hyperparameterwahl automatisieren Exercise 11: Lernkurven erstellen

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: Introducing Grid Search Exercise 2: Build Grid Search functions Exercise 3: Iteratively tune multiple hyperparameters Exercise 4: How Many Models?Exercise 5: Grid Search with Scikit Learn Exercise 6: GridSearchCV inputs Exercise 7: GridSearchCV with Scikit Learn Exercise 8: Understanding a grid search output Exercise 9: Using the best outputs Exercise 10: Exploring the grid search results Exercise 11: Analyzing the best results Exercise 12: Using the best results

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!