Apakah saya underfitting?

Anda membuat model random forest untuk memprediksi apakah Anda akan menang dalam permainan Tic-Tac-Toe di masa depan. Dengan menggunakan himpunan data tic_tac_toe, Anda telah membuat himpunan data pelatihan dan pengujian, yaitu X_train, X_test, y_train, dan y_test.

Anda memutuskan untuk membuat sejumlah model random forest dengan jumlah pohon yang bervariasi (1, 2, 3, 4, 5, 10, 20, dan 50). Semakin banyak pohon yang Anda gunakan, semakin lama waktu eksekusi model random forest Anda. Namun, jika jumlah pohon tidak cukup, Anda berisiko mengalami underfitting. Anda telah membuat sebuah perulangan for untuk menguji model pada berbagai jumlah pohon tersebut.

Latihan ini adalah bagian dari kursus

Validasi Model di Python

Petunjuk latihan

Pada setiap iterasi, prediksikan nilai untuk himpunan data X_train dan X_test.
Pada setiap iterasi, tambahkan accuracy_score() dari himpunan data y_train dan prediksi yang sesuai ke train_scores.
Pada setiap iterasi, tambahkan accuracy_score() dari himpunan data y_test dan prediksi yang sesuai ke test_scores.
Cetak skor pelatihan dan pengujian menggunakan pernyataan print yang disediakan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

from sklearn.metrics import accuracy_score

test_scores, train_scores = [], []
for i in [1, 2, 3, 4, 5, 10, 20, 50]:
    rfc = RandomForestClassifier(n_estimators=i, random_state=1111)
    rfc.fit(X_train, y_train)
    # Create predictions for the X_train and X_test datasets.
    train_predictions = rfc.predict(____)
    test_predictions = rfc.predict(____)
    # Append the accuracy score for the test and train predictions.
    train_scores.append(round(____(____, ____), 2))
    test_scores.append(round(____(____, ____), 2))
# Print the train and test scores.
print("The training scores were: {}".format(____))
print("The testing scores were: {}".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 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?

Latihan Saat Ini

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!