Aturan Bayes di Python

Dalam latihan ini Anda akan melakukan contoh praktis menyusun rumus Bayes, memperoleh bukti baru, dan memperbarui ‘keyakinan’ Anda untuk mendapatkan hasil yang lebih akurat. Contoh ini berkaitan dengan kemungkinan seseorang menutup akun mereka untuk produk perangkat lunak online Anda.

Berikut probabilitas yang kita ketahui:

7% (0,07) orang berpeluang menutup akun mereka bulan depan
15% (0,15) orang yang memiliki akun tidak puas dengan produk Anda (namun Anda tidak tahu siapa!)
35% (0,35) orang yang berpeluang menutup akun tidak puas dengan produk Anda

Latihan ini adalah bagian dari kursus

Penyetelan Hyperparameter di Python

Lihat Kursus

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Assign probabilities to variables 
p_unhappy = ____
p_unhappy_close = ____

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Penyetelan Hyperparameter di Python

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Mulai Kursus Gratis

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: 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: Dari Kasar ke Rinci Exercise 2: Memvisualisasikan Coarse to Fine Exercise 3: Iterasi Coarse to Fine Exercise 4: Informed Search: Statistika Bayesian Exercise 5: Aturan Bayes di Python

Latihan Saat Ini

Exercise 6: Penyetelan Hyperparameter Bayesian dengan Hyperopt Exercise 7: Pencarian Terinformasi: Algoritme Genetika Exercise 8: Penyetelan Hyperparameter Genetik dengan TPOT Exercise 9: Menganalisis kestabilan TPOT Exercise 10: Selamat!