Menjalankan model menggunakan rentang

Anda baru saja selesai membuat daftar hyperparameter dan rentang yang akan digunakan saat melakukan tuning model prediktif untuk sebuah tugas. Anda menggunakan max_depth, min_samples_split, dan max_features sebagai nama variabel rentang Anda.

Latihan ini adalah bagian dari kursus

Validasi Model di Python

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Petunjuk latihan

Pilih secara acak max_depth, min_samples_split, dan max_features menggunakan variabel rentang Anda.
Cetak semua parameter untuk rfr guna melihat nilai mana yang dipilih secara acak.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

from sklearn.ensemble import RandomForestRegressor

# Fill in rfr using your variables
rfr = RandomForestRegressor(
    n_estimators=100,
    max_depth=random.____(____),
    min_samples_split=random.____(____),
    max_features=random.____(____))

# Print out the parameters
print(rfr.____)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Validasi Model di Python

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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: 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: Pengantar hyperparameter tuning Exercise 2: Membuat Hyperparameter Exercise 3: Menjalankan model menggunakan rentang

Latihan Saat Ini

Exercise 4: RandomizedSearchCV Exercise 5: Mempersiapkan RandomizedSearch Exercise 6: Menerapkan RandomizedSearchCV Exercise 7: Memilih model final Anda Exercise 8: Akurasi klasifikasi terbaik Exercise 9: Memilih model precision terbaik Exercise 10: Kursus selesai!