Mean squared error

Mari kembali fokus pada prediksi NBA tahun 2017. Setiap tahun, selalu ada setidaknya beberapa tim NBA yang menang jauh lebih banyak dari perkiraan. Jika Anda menggunakan MAE, metrik akurasi ini tidak terlalu mencerminkan prediksi yang buruk dibandingkan jika Anda menggunakan MSE. Menguadratkan galat besar dari prediksi yang buruk akan membuat nilai akurasi tampak lebih buruk.

Dalam contoh ini, eksekutif NBA ingin memprediksi jumlah kemenangan tim dengan lebih baik. Anda akan menggunakan mean squared error untuk menghitung galat prediksi. Jumlah kemenangan aktual telah dimuat sebagai y_test dan prediksi sebagai predictions.

Latihan ini adalah bagian dari kursus

Validasi Model di Python

Petunjuk latihan

Hitung MSE secara manual. $$ MSE = \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i ) ^2 }{n} $$
Hitung MSE menggunakan sklearn.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

from sklearn.metrics import ____

n = ___(predictions)
# Finish the manual calculation of the MSE
mse_one = sum((y_test - predictions)____) / n
print('With a manual calculation, the error is {}'.format(mse_one))

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

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 Exercise 7: Mean squared error

Latihan Saat Ini

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!