Regressione logistica vs albero di classificazione

Un albero di classificazione suddivide lo spazio delle feature in regioni rettangolari. Al contrario, un modello lineare come la regressione logistica produce un'unica frontiera di decisione lineare che divide lo spazio delle feature in due regioni di decisione.

Abbiamo scritto una funzione personalizzata chiamata plot_labeled_decision_regions() che puoi usare per tracciare le regioni di decisione di una lista contenente due classificatori già addestrati. Puoi digitare help(plot_labeled_decision_regions) nella shell per saperne di più su questa funzione.

X_train, X_test, y_train, y_test, il modello dt che hai addestrato in un esercizio precedente, così come la funzione plot_labeled_decision_regions(), sono disponibili nel tuo workspace.

Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

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Istruzioni dell'esercizio

Importa LogisticRegression da sklearn.linear_model.
Istanzia un modello LogisticRegression e assegnalo a logreg.
Esegui il fit di logreg sul training set.
Osserva il grafico generato da plot_labeled_decision_regions().

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import LogisticRegression from sklearn.linear_model
from ____.____ import  ____

# Instatiate logreg
____ = ____(random_state=1)

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

# Define a list called clfs containing the two classifiers logreg and dt
clfs = [logreg, dt]

# Review the decision regions of the two classifiers
plot_labeled_decision_regions(X_test, y_test, clfs)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

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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: Albero decisionale per la classificazione Exercise 2: Allena il tuo primo albero di classificazione Exercise 3: Valuta l'albero di classificazione Exercise 4: Regressione logistica vs albero di classificazione

Esercizio in corso

Exercise 5: Apprendimento dell'albero di classificazione Exercise 6: Far crescere un albero di classificazione Exercise 7: Usare l'entropia come criterio Exercise 8: Entropia vs indice di Gini Exercise 9: Albero decisionale per la regressione Exercise 10: Allena il tuo primo albero di regressione Exercise 11: Valuta l’albero di regressione Exercise 12: Regressione lineare vs albero di regressione

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!