Menerapkan cross_val_score()

Perusahaan Anda telah membuat beberapa permen baru untuk dijual, tetapi mereka belum yakin apakah harus merilis kelimanya. Untuk memprediksi popularitas permen-permen baru ini, Anda diminta membangun model regresi menggunakan himpunan data permen. Ingat bahwa nilai respons adalah persentase kemenangan head-to-head terhadap permen lainnya.

Sebelum Anda mulai mencoba berbagai model regresi, Anda memutuskan untuk menjalankan cross-validation pada model random forest sederhana untuk memperoleh galat dasar sebagai pembanding bagi hasil-hasil selanjutnya.

Latihan ini adalah bagian dari kursus

Validasi Model di Python

Petunjuk latihan

Lengkapi cross_val_score().
- Gunakan X_train untuk data pelatihan, dan y_train untuk respons.
- Gunakan rfc sebagai model, cross-validation 10-fold, dan mse sebagai fungsi penilaian.
Cetak nilai rata-rata dari hasil cv.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

rfc = RandomForestRegressor(n_estimators=25, random_state=1111)
mse = make_scorer(mean_squared_error)

# Set up cross_val_score
cv = cross_val_score(estimator=____,
                     X=____,
                     y=____,
                     cv=____,
                     scoring=____)

# Print the mean error
print(cv.____())

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: Creating train, test, and validation datasets Exercise 2: Create one holdout set Exercise 3: Create two holdout sets Exercise 4: Why use holdout sets Exercise 5: Accuracy metrics: regression models Exercise 6: Mean absolute error Exercise 7: Mean squared error Exercise 8: Performance on data subsets Exercise 9: Classification metrics Exercise 10: Confusion matrices Exercise 11: Confusion matrices, again Exercise 12: Precision vs. recall Exercise 13: The bias-variance tradeoff Exercise 14: Error due to under/over-fitting Exercise 15: Am I 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: Masalah pada holdout set Exercise 2: Dua sampel Exercise 3: Potensi masalah Exercise 4: Cross-validation Exercise 5: KFold() di scikit-learn Exercise 6: Menggunakan indeks KFold Exercise 7: cross_val_score() dari sklearn Exercise 8: Metode-metode scikit-learn Exercise 9: Menerapkan cross_val_score()

Latihan Saat Ini

Exercise 10: Leave-one-out cross-validation (LOOCV)Exercise 11: Kapan menggunakan 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!