Evaluasi pohon optimal

Pada latihan ini, Anda akan mengevaluasi skor ROC AUC himpunan uji dari model optimal milik grid_dt.

Untuk melakukannya, Anda terlebih dahulu akan menentukan probabilitas memperoleh label positif untuk setiap observasi pada himpunan uji. Anda dapat menggunakan metode predict_proba() dari suatu pengklasifikasi scikit-learn untuk menghitung array 2D yang berisi probabilitas label kelas negatif dan positif masing-masing pada kolom.

Himpunan data sudah dimuat dan diproses untuk Anda (fitur numerik telah distandardisasi); data dibagi menjadi 80% latih dan 20% uji. X_test, y_test tersedia di ruang kerja Anda. Selain itu, kami juga telah memuat objek GridSearchCV terlatih grid_dt yang Anda inisialisasi pada latihan sebelumnya. Perhatikan bahwa grid_dt dilatih sebagai berikut:

grid_dt.fit(X_train, y_train)

Latihan ini adalah bagian dari kursus

Machine Learning dengan Model Berbasis Pohon di Python

Petunjuk latihan

Impor roc_auc_score dari sklearn.metrics.
Ekstrak atribut .best_estimator_ dari grid_dt dan tetapkan ke best_model.
Prediksi probabilitas himpunan uji untuk memperoleh kelas positif y_pred_proba.
Hitung skor ROC AUC himpunan uji test_roc_auc dari best_model.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import roc_auc_score from sklearn.metrics
____

# Extract the best estimator
best_model = ____

# Predict the test set probabilities of the positive class
y_pred_proba = ____

# Compute test_roc_auc
test_roc_auc = ____

# Print test_roc_auc
print('Test set ROC AUC score: {:.3f}'.format(test_roc_auc))

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Machine Learning dengan Model Berbasis Pohon di Python

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus Gratis

Classification and Regression Trees (CART) are a set of supervised learning models used for problems involving classification and regression. In this chapter, you'll be introduced to the CART algorithm.

Exercise 1: Decision tree for classification Exercise 2: Train your first classification tree Exercise 3: Evaluate the classification tree Exercise 4: Logistic regression vs classification tree Exercise 5: Classification tree Learning Exercise 6: Growing a classification tree Exercise 7: Using entropy as a criterion Exercise 8: Entropy vs Gini index Exercise 9: Decision tree for regression Exercise 10: Train your first regression tree Exercise 11: Evaluate the regression tree Exercise 12: Linear regression vs regression tree

The bias-variance tradeoff is one of the fundamental concepts in supervised machine learning. In this chapter, you'll understand how to diagnose the problems of overfitting and underfitting. You'll also be introduced to the concept of ensembling where the predictions of several models are aggregated to produce predictions that are more robust.

Exercise 1: Generalization Error Exercise 2: Complexity, bias and variance Exercise 3: Overfitting and underfitting Exercise 4: Diagnose bias and variance problems Exercise 5: Instantiate the model Exercise 6: Evaluate the 10-fold CV error Exercise 7: Evaluate the training error Exercise 8: High bias or high variance?Exercise 9: Ensemble Learning Exercise 10: Define the ensemble Exercise 11: Evaluate individual classifiers Exercise 12: Better performance with a Voting Classifier

Bagging is an ensemble method involving training the same algorithm many times using different subsets sampled from the training data. In this chapter, you'll understand how bagging can be used to create a tree ensemble. You'll also learn how the random forests algorithm can lead to further ensemble diversity through randomization at the level of each split in the trees forming the ensemble.

Exercise 1: Bagging Exercise 2: Define the bagging classifier Exercise 3: Evaluate Bagging performance Exercise 4: Out of Bag Evaluation Exercise 5: Prepare the ground Exercise 6: OOB Score vs Test Set Score Exercise 7: Random Forests (RF)Exercise 8: Train an RF regressor Exercise 9: Evaluate the RF regressor Exercise 10: Visualizing features importances

Boosting refers to an ensemble method in which several models are trained sequentially with each model learning from the errors of its predecessors. In this chapter, you'll be introduced to the two boosting methods of AdaBoost and Gradient Boosting.

Exercise 1: Adaboost Exercise 2: Define the AdaBoost classifier Exercise 3: Train the AdaBoost classifier Exercise 4: Evaluate the AdaBoost classifier Exercise 5: Gradient Boosting (GB)Exercise 6: Define the GB regressor Exercise 7: Train the GB regressor Exercise 8: Evaluate the GB regressor Exercise 9: Stochastic Gradient Boosting (SGB)Exercise 10: Regression with SGB Exercise 11: Train the SGB regressor Exercise 12: Evaluate the SGB regressor

The hyperparameters of a machine learning model are parameters that are not learned from data. They should be set prior to fitting the model to the training set. In this chapter, you'll learn how to tune the hyperparameters of a tree-based model using grid search cross validation.

Exercise 1: Menyetel Hyperparameter CART Exercise 2: Hiperparameter pohon Exercise 3: Atur grid hyperparameter untuk tree Exercise 4: Cari pohon yang optimal Exercise 5: Evaluasi pohon optimal

Latihan Saat Ini

Exercise 6: Menyetel Hyperparameter RF Exercise 7: Hyperparameter random forests Exercise 8: Tetapkan grid hyperparameter untuk RF Exercise 9: Cari hutan yang optimal Exercise 10: Evaluasi hutan optimal Exercise 11: Selamat!