Evaluasi galat pelatihan

Sekarang Anda akan mengevaluasi RMSE himpunan latih yang dicapai oleh pohon regresi dt yang telah Anda inisialisasi pada latihan sebelumnya.

Selain dt, X_train dan y_train tersedia di workspace Anda.

Perhatikan bahwa di scikit-learn, MSE sebuah model dapat dihitung sebagai berikut:

MSE_model = mean_squared_error(y_true, y_predicted)

di mana kita menggunakan fungsi mean_squared_error dari modul metrics dan memberikan label sebenarnya y_true sebagai argumen pertama, serta label prediksi dari model y_predicted sebagai argumen kedua.

Latihan ini adalah bagian dari kursus

Machine Learning dengan Model Berbasis Pohon di Python

Petunjuk latihan

Impor mean_squared_error sebagai MSE dari sklearn.metrics.
Latih dt pada himpunan latih.
Prediksi label himpunan latih dengan dt dan simpan hasilnya ke y_pred_train.
Evaluasi RMSE himpunan latih dt dan simpan ke RMSE_train.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import mean_squared_error from sklearn.metrics as MSE
____

# Fit dt to the training set
____.____(____, ____)

# Predict the labels of the training set
____ = ____.____(____)

# Evaluate the training set RMSE of dt
____ = (____(____, ____))**(___)

# Print RMSE_train
print('Train RMSE: {:.2f}'.format(RMSE_train))

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: Galat Generalisasi Exercise 2: Kompleksitas, bias, dan varians Exercise 3: Overfitting dan underfitting Exercise 4: Mendiagnosis masalah bias dan varians Exercise 5: Inisialisasi model Exercise 6: Evaluasi galat CV 10-fold Exercise 7: Evaluasi galat pelatihan

Latihan Saat Ini

Exercise 8: Bias tinggi atau varians tinggi?Exercise 9: Ensemble Learning Exercise 10: Mendefinisikan ensemble Exercise 11: Evaluasi pengklasifikasi individual Exercise 12: Kinerja lebih baik dengan 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!