Filter spam Bayesian

Bagus sekali pada latihan sebelumnya! Sekarang mari kita bahas Teorema Bayes yang terkenal dan menggunakannya untuk tugas yang sederhana namun penting: deteksi spam.

Saat menelusuri kotak masuk, Anda menyadari bahwa cukup banyak email yang sebaiknya tidak Anda buang waktu untuk membacanya memuat kalimat seru, seperti "BELI SEKARANG!!!". Anda mulai berpikir bahwa keberadaan tiga tanda seru berurutan bisa menjadi prediktor spam yang baik! Karena itu Anda menyiapkan sebuah DataFrame bernama emails dengan dua variabel: spam, apakah email tersebut spam, dan contains_3_exlc, apakah email tersebut memuat string "!!!". Bagian awal datanya terlihat seperti ini:

     spam    contains_3_excl
0    False             False
1    False             False
2    True              False
3    False             False
4    False             False

Tugas Anda adalah menghitung probabilitas suatu email merupakan spam dengan syarat email tersebut memuat tiga tanda seru. Mari kita selesaikan langkah demi langkah! Berikut adalah rumus Bayes sebagai referensi:

$$P(A|B) = \frac{P(B|A) * P(A)}{P(B)}$$

Latihan ini adalah bagian dari kursus

Analisis Data Bayesian dengan Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Calculate and print the unconditional probability of spam
p_spam = ____[____].____
print(____)

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: Siapa Bayes? Apa itu Bayes?Exercise 2: Bayesian vs. Frequentist Exercise 3: Sebaran probabilitas Exercise 4: Probabilitas dan Teorema Bayes Exercise 5: Ayo bermain kartu Exercise 6: Filter spam Bayesian

Latihan Saat Ini

Exercise 7: Apa kata hasil tes?Exercise 8: Mencicipi Bayes Exercise 9: Melempar koin Exercise 10: Semakin sering Anda melempar, semakin banyak Anda belajar Exercise 11: Hei, apakah koin ini adil?

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 and model fitting Exercise 2: Markov Chain Monte Carlo Exercise 3: Sampling posterior draws Exercise 4: Interpreting results and comparing models Exercise 5: Inspecting posterior draws Exercise 6: Comparing models with WAIC Exercise 7: Making predictions Exercise 8: Sample from predictive density Exercise 9: Estimating test error Exercise 10: How much is an avocado?Exercise 11: Fitting the model Exercise 12: Inspecting the model Exercise 13: Optimizing the price Exercise 14: Final remarks