Mean absolute error

Mengomunikasikan hasil pemodelan bisa jadi menantang. Namun, sebagian besar klien memahami bahwa, rata-rata, sebuah model prediktif meleset sebesar sejumlah tertentu. Hal ini membuat penjelasan mean absolute error menjadi mudah. Misalnya, saat memprediksi jumlah kemenangan sebuah tim basket, jika Anda memprediksi 42, dan mereka berakhir dengan 40, Anda dapat dengan mudah menjelaskan bahwa galatnya adalah dua kemenangan.

Dalam latihan ini, Anda sedang mengikuti wawancara untuk posisi baru dan disediakan dua array: y_test, jumlah kemenangan sebenarnya untuk seluruh 30 tim NBA pada 2017, dan predictions, yang memuat satu prediksi untuk setiap tim. Untuk menguji pemahaman Anda, Anda diminta untuk menghitung MAE secara manual dan menggunakan sklearn.

Latihan ini adalah bagian dari kursus

Validasi Model di Python

Petunjuk latihan

Hitung MAE secara manual dengan menggunakan n sebagai jumlah observasi yang diprediksi.
Hitung MAE menggunakan sklearn.
Cetak kedua nilai akurasi menggunakan pernyataan print.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

from sklearn.metrics import mean_absolute_error

# Manually calculate the MAE
n = ____(predictions)
mae_one = sum(____(y_test - predictions)) / n
print('With a manual calculation, the error is {}'.format(____))

# Use scikit-learn to calculate the MAE
mae_two = ____(____, ____)
print('Using scikit-learn, the error is {}'.format(____))

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Validasi Model di Python

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus Gratis

Before we can validate models, we need an understanding of how to create and work with them. This chapter provides an introduction to running regression and classification models in scikit-learn. We will use this model building foundation throughout the remaining chapters.

Exercise 1: Introduction to model validation Exercise 2: Modeling steps Exercise 3: Seen vs. unseen data Exercise 4: Regression models Exercise 5: Set parameters and fit a model Exercise 6: Feature importances Exercise 7: Classification models Exercise 8: Classification predictions Exercise 9: Reusing model parameters Exercise 10: Random forest classifier

This chapter focuses on the basics of model validation. From splitting data into training, validation, and testing datasets, to creating an understanding of the bias-variance tradeoff, we build the foundation for the techniques of K-Fold and Leave-One-Out validation practiced in chapter three.

Exercise 1: Membuat himpunan data train, test, dan validation Exercise 2: Buat satu holdout set Exercise 3: Buat dua himpunan holdout Exercise 4: Mengapa menggunakan holdout set Exercise 5: Metrik akurasi: model regresi Exercise 6: Mean absolute error

Latihan Saat Ini

Exercise 7: Mean squared error Exercise 8: Kinerja pada subset data Exercise 9: Metrik klasifikasi Exercise 10: Confusion matrix Exercise 11: Matriks kebingungan, lagi Exercise 12: Precision vs. recall Exercise 13: Pertukaran bias-varians Exercise 14: Galat akibat under/overfitting Exercise 15: Apakah saya underfitting?

Holdout sets are a great start to model validation. However, using a single train and test set if often not enough. Cross-validation is considered the gold standard when it comes to validating model performance and is almost always used when tuning model hyper-parameters. This chapter focuses on performing cross-validation to validate model performance.

Exercise 1: The problems with holdout sets Exercise 2: Two samples Exercise 3: Potential problems Exercise 4: Cross-validation Exercise 5: scikit-learn's KFold()Exercise 6: Using KFold indices Exercise 7: sklearn's cross_val_score()Exercise 8: scikit-learn's methods Exercise 9: Implement cross_val_score()Exercise 10: Leave-one-out-cross-validation (LOOCV)Exercise 11: When to use LOOCV Exercise 12: Leave-one-out-cross-validation

The first three chapters focused on model validation techniques. In chapter 4 we apply these techniques, specifically cross-validation, while learning about hyperparameter tuning. After all, model validation makes tuning possible and helps us select the overall best model.

Exercise 1: Introduction to hyperparameter tuning Exercise 2: Creating Hyperparameters Exercise 3: Running a model using ranges Exercise 4: RandomizedSearchCV Exercise 5: Preparing for RandomizedSearch Exercise 6: Implementing RandomizedSearchCV Exercise 7: Selecting your final model Exercise 8: Best classification accuracy Exercise 9: Selecting the best precision model Exercise 10: Course completed!