Berbagai metode chunking

Satu chunk merepresentasikan satu titik data dalam hasil pemantauan. Ingat bahwa ada tiga metode untuk melakukan chunking pada data Anda: berdasarkan waktu, ukuran, atau jumlah chunk.

Dalam latihan ini, Anda akan melakukan chunking dan memvisualisasikan hasil algoritma CBPE untuk himpunan data US Census menggunakan metode chunking berbasis ukuran dan berbasis jumlah.

Pustaka nannyml sudah diimpor.

Latihan ini adalah bagian dari kursus

Pemantauan Machine Learning dengan Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

reference, analysis, analysis_gt = ____.____()

# Initialize the CBPE algorithm
cbpe = nannyml.CBPE(
    y_pred_proba='predicted_probability',
    y_pred='prediction',
    y_true='employed',
    metrics = ['roc_auc', 'accuracy'],
    problem_type = 'classification_binary',
    ____ = ____,
)

cbpe = cbpe.fit(reference)
estimated_results = cbpe.estimate(analysis)
estimated_results.plot().show()

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pemantauan Machine Learning dengan Python

SkillTag.level.advancedSkillTag.label

4.8+

Mulai Kursus Gratis

In this chapter, you will be introduced to the NannyML library and its fundamental functions. Initially, you will learn the process of preparing raw data to create reference and analysis sets ready for production monitoring. As a practical example, you will investigate predicting the tip amount for taxi rides in New York. Toward the end of the chapter, you will also discover how to estimate the performance of the tip prediction model using NannyML.

Exercise 1: What is NannyML?Exercise 2: Key features of NannyML Exercise 3: Load the dataset Exercise 4: Data preparation for NannyML Exercise 5: Reference or analysis period?Exercise 6: Loading and splitting the data Exercise 7: Creating reference and analysis set Exercise 8: Performance estimation Exercise 9: Specify the algorithm and problem type Exercise 10: Interpreting results Exercise 11: CBPE and DLE workflow Exercise 12: Performance estimation for tip prediction

In this chapter, you will be introduced to realized performance calculators used when ground truth becomes available. You will learn about the more advanced methods for handling results, including filtering, plotting, converting them to data frames, chunking, and establishing custom thresholds. Lastly, you'll apply this knowledge to calculate the business value of a model trained on the hotel booking dataset.

Exercise 1: Ketika label tersedia Exercise 2: Saat estimasi kinerja meleset Exercise 3: Membandingkan performa terestimasi dan terealisasi Exercise 4: Bekerja dengan hasil yang dihitung dan diestimasi Exercise 5: Berbagai metode chunking

Latihan Saat Ini

Exercise 6: Memodifikasi ambang batas Exercise 7: Berinteraksi dengan hasil Exercise 8: Perhitungan dan estimasi nilai bisnis Exercise 9: Perhitungan nilai bisnis Exercise 10: Penurunan nilai moneter Exercise 11: Perhitungan bisnis untuk himpunan data pemesanan hotel

Having detected the performance degradation in the hotel booking model, you will now learn how to identify the underlying issue causing it. In this chapter, you will be introduced to multivariate and univariate drift detection methods. You will also learn how to identify data quality issues and how to address the underlying problems you detect.

Exercise 1: Multivariate drift detection Exercise 2: Identifying relevant drifts Exercise 3: Drift in hotel booking dataset Exercise 4: Univariate drift detection Exercise 5: Univariate drift detection for hotel booking dataset Exercise 6: Ranking the univariate results Exercise 7: Visualizing drifting features Exercise 8: Data quality and statistic checks Exercise 9: Data quality checks Exercise 10: Summary statistics Exercise 11: Issue resolution Exercise 12: What is the resolution?Exercise 13: Should you do nothing or not?Exercise 14: Implementing a monitoring workflow Exercise 15: Congratulations