Evaluasi pohon klasifikasi

Sekarang setelah Anda melatih pohon klasifikasi pertama, saatnya mengevaluasi kinerjanya pada himpunan uji. Anda akan melakukannya menggunakan metrik akurasi yang menunjukkan porsi prediksi benar pada himpunan uji.

Model terlatih dt dari latihan sebelumnya telah dimuat di ruang kerja Anda bersama dengan matriks fitur himpunan uji X_test dan larik label y_test.

Latihan ini adalah bagian dari kursus

Machine Learning dengan Model Berbasis Pohon di Python

Petunjuk latihan

Impor fungsi accuracy_score dari sklearn.metrics.
Prediksikan label himpunan uji dan tetapkan larik yang diperoleh ke y_pred.
Evaluasi skor akurasi himpunan uji dari dt dengan memanggil accuracy_score() dan tetapkan nilainya ke acc.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import accuracy_score
from ____.____ import ____

# Predict test set labels
y_pred = ____.____(____)

# Compute test set accuracy  
acc = ____(____, ____)
print("Test set accuracy: {:.2f}".format(acc))

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: Pohon keputusan untuk klasifikasi Exercise 2: Latih pohon klasifikasi pertama Anda Exercise 3: Evaluasi pohon klasifikasi

Latihan Saat Ini

Exercise 4: Logistic regression vs classification tree Exercise 5: Pembelajaran pohon klasifikasi Exercise 6: Membangun pohon klasifikasi Exercise 7: Menggunakan entropy sebagai kriteria Exercise 8: Entropi vs Indeks Gini Exercise 9: Pohon keputusan untuk regresi Exercise 10: Latih pohon regresi pertama Anda Exercise 11: Evaluasi pohon regresi Exercise 12: Regresi linear vs pohon regresi

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: Tuning a CART's Hyperparameters Exercise 2: Tree hyperparameters Exercise 3: Set the tree's hyperparameter grid Exercise 4: Search for the optimal tree Exercise 5: Evaluate the optimal tree Exercise 6: Tuning a RF's Hyperparameters Exercise 7: Random forests hyperparameters Exercise 8: Set the hyperparameter grid of RF Exercise 9: Search for the optimal forest Exercise 10: Evaluate the optimal forest Exercise 11: Congratulations!