Memvisualisasikan pentingnya fitur

Dalam latihan ini, Anda akan menentukan fitur mana yang paling prediktif menurut random forests regressor rf yang telah Anda latih pada latihan sebelumnya.

Untuk tujuan ini, Anda akan menggambar barplot horizontal dari tingkat kepentingan fitur sebagaimana dinilai oleh rf. Untungnya, hal ini dapat dilakukan dengan mudah berkat kemampuan plotting dari pandas.

Kami telah membuat objek pandas.Series bernama importances yang berisi nama fitur sebagai index dan nilai kepentingannya sebagai value. Selain itu, matplotlib.pyplot tersedia sebagai plt dan pandas sebagai pd.

Latihan ini adalah bagian dari kursus

Machine Learning dengan Model Berbasis Pohon di Python

Petunjuk latihan

Panggil metode .sort_values() pada importances dan simpan hasilnya ke importances_sorted.
Panggil metode .plot() pada importances_sorted dan atur argumen:
- kind menjadi 'barh'
- color menjadi 'lightgreen'

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create a pd.Series of features importances
importances = pd.Series(data=rf.feature_importances_,
                        index= X_train.columns)

# Sort importances
importances_sorted = ____

# Draw a horizontal barplot of importances_sorted
____.____(____='____', ____='____')
plt.title('Features Importances')
plt.show()

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: Definisikan bagging classifier Exercise 3: Evaluasi kinerja Bagging Exercise 4: Evaluasi Out-of-Bag Exercise 5: Persiapkan landasan Exercise 6: Skor OOB vs Skor Test Set Exercise 7: Random Forests (RF)Exercise 8: Latih sebuah regresor RF Exercise 9: Evaluasi RF regressor Exercise 10: Memvisualisasikan pentingnya fitur

Latihan Saat Ini

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!