Mengestimasi galat uji

Sekarang Anda telah memiliki posterior_predictive (tersedia di ruang kerja Anda), Anda dapat mengevaluasi kinerja model pada data baru. Untuk melakukannya, Anda perlu melakukan perulangan pada observasi uji, dan untuk masing-masing observasi, hitung galat prediksi sebagai selisih antara sebaran prediktif untuk observasi tersebut dan nilai sebenarnya. Ini akan memberikan sebaran galat model Anda, yang kemudian dapat Anda visualisasikan.

Anda akan memerlukan pymc3 dan numpy, yang telah diimpor sebagai pm dan np. Data uji, bikes_test, juga tersedia di ruang kerja Anda. Mari kita mulai!

Latihan ini adalah bagian dari kursus

Analisis Data Bayesian dengan Python

Petunjuk latihan

Inisialisasi errors sebagai list kosong.
Untuk setiap baris di bikes_test, hitung galat prediksi sebagai pengambilan prediktif untuk baris ini dari posterior_predictive dikurangi satu nilai sebenarnya num_bikes dari baris tersebut.
Bentuk ulang errors dengan mengonversinya menjadi array numpy dan menerapkan metode .reshape() pada hasilnya, lalu tetapkan hasil akhir ke error_distribution.
Plot sebaran galat uji menggunakan fungsi plot_posterior() dari pymc3.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Initialize errors
errors = ____

# Iterate over rows of bikes_test to compute error per row
for index, test_example in bikes_test.iterrows():
    error = ____[____][:, ____] - ____[____]
    errors.append(error)

# Reshape errors
error_distribution = ____(____).____()

# Plot the error distribution
____
plt.show()

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Analisis Data Bayesian dengan Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

Take your first steps in the Bayesian world. In this chapter, you’ll be introduced to the basic concepts of probability and statistical distributions, as well as to the famous Bayes' Theorem, the cornerstone of Bayesian methods. Finally, you’ll build your first Bayesian model to draw conclusions from randomized coin tosses.

Exercise 1: Who is Bayes? What is Bayes?Exercise 2: Bayesians vs. Frequentists Exercise 3: Probability distributions Exercise 4: Probability and Bayes' Theorem Exercise 5: Let's play cards Exercise 6: Bayesian spam filter Exercise 7: What does the test say?Exercise 8: Tasting the Bayes Exercise 9: Tossing a coin Exercise 10: The more you toss, the more you learn Exercise 11: Hey, is this coin fair?

It’s time to look under the Bayesian hood. You’ll learn how to apply Bayes' Theorem to drug-effectiveness data to estimate the parameters of probability distributions using the grid approximation technique, and update these estimates as new data become available. Next, you’ll learn how to incorporate prior knowledge into the model before finally practicing the important skill of reporting results to a non-technical audience.

Exercise 1: Under the Bayesian hood Exercise 2: Towards grid approximation Exercise 3: Grid approximation without prior knowledge Exercise 4: Updating posterior belief Exercise 5: Prior belief Exercise 6: The truth of the prior Exercise 7: Picking the right prior Exercise 8: Simulating posterior draws Exercise 9: Reporting Bayesian results Exercise 10: Point estimates Exercise 11: Highest Posterior Density credible intervals Exercise 12: The meaning of credibility

Apply your newly acquired Bayesian data analysis skills to solve real-world business challenges. You’ll work with online sales marketing data to conduct A/B tests, decision analysis, and forecasting with linear regression models.

Exercise 1: A/B testing Exercise 2: Simulate beta posterior Exercise 3: Posterior click rates Exercise 4: A or B, and how sure are we?Exercise 5: How bad can it be?Exercise 6: Decision analysis Exercise 7: Decision analysis: cost Exercise 8: Decision analysis: profit Exercise 9: Regression and forecasting Exercise 10: Defining a Bayesian regression model Exercise 11: Analyzing regression parameters Exercise 12: Predictive distribution

In this final chapter, you’ll take advantage of the powerful PyMC3 package to easily fit Bayesian regression models, conduct sanity checks on a model's convergence, select between competing models, and generate predictions for new data. To wrap up, you’ll apply what you’ve learned to find the optimal price for avocados in a Bayesian data analysis case study. Good luck!

Exercise 1: Markov Chain Monte Carlo dan pemodelan Exercise 2: Markov Chain Monte Carlo Exercise 3: Melakukan sampling dari posterior Exercise 4: Menafsirkan hasil dan membandingkan model Exercise 5: Memeriksa sampel posterior Exercise 6: Membandingkan model dengan WAIC Exercise 7: Membuat prediksi Exercise 8: Sampel dari kerapatan prediktif Exercise 9: Mengestimasi galat uji

Latihan Saat Ini

Exercise 10: Berapa harga sebuah alpukat?Exercise 11: Memasang model Exercise 12: Memeriksa model Exercise 13: Mengoptimalkan harga Exercise 14: Catatan penutup