Regressione Lasso per l'importanza delle caratteristiche

Nel video hai visto come la regressione lazo può essere utilizzata per identificare le caratteristiche importanti in un set di dati.

In questa esercitazione, dovrai applicare un modello di regressione lasso ai dati di sales_df e tracciare i coefficienti del modello.

Gli array di variabili caratteristiche e target sono stati precaricati come X e y, insieme a sales_columns, che contiene i nomi delle caratteristiche del dataset.

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Istruzioni dell'esercizio

Importa Lasso da sklearn.linear_model.
Istanziare un regressore Lasso con un alfa di 0.3.
Adatta il modello ai dati.
Calcola i coefficienti del modello, memorizzandoli come lasso_coef.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import Lasso
from ____.____ import ____

# Instantiate a lasso regression model
lasso = ____

# Fit the model to the data
____

# Compute and print the coefficients
lasso_coef = ____
print(lasso_coef)
plt.bar(sales_columns, lasso_coef)
plt.xticks(rotation=45)
plt.show()

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Apprendimento supervisionato con scikit-learn

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In this chapter, you'll be introduced to classification problems and learn how to solve them using supervised learning techniques. You'll learn how to split data into training and test sets, fit a model, make predictions, and evaluate accuracy. You’ll discover the relationship between model complexity and performance, applying what you learn to a churn dataset, where you will classify the churn status of a telecom company's customers.

Exercise 1: Machine learning with scikit-learn Exercise 2: Binary classification Exercise 3: The supervised learning workflow Exercise 4: The classification challenge Exercise 5: k-Nearest Neighbors: Fit Exercise 6: k-Nearest Neighbors: Predict Exercise 7: Measuring model performance Exercise 8: Train/test split + computing accuracy Exercise 9: Overfitting and underfitting Exercise 10: Visualizing model complexity

In this chapter, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting.

Exercise 1: Introduzione alla regressione Exercise 2: Creazione di caratteristiche Exercise 3: Costruire un modello di regressione lineare Exercise 4: Visualizzazione di un modello di regressione lineare Exercise 5: Le basi della regressione lineare Exercise 6: Adattamento e previsione per la regressione Exercise 7: Prestazioni di regressione Exercise 8: Convalida incrociata Exercise 9: Convalida incrociata per l'R-squared Exercise 10: Analisi delle metriche di convalida incrociata Exercise 11: Regressione regolarizzata Exercise 12: Regressione regolarizzata: Crinale Exercise 13: Regressione Lasso per l'importanza delle caratteristiche

Esercizio in corso

Having trained models, now you will learn how to evaluate them. In this chapter, you will be introduced to several metrics along with a visualization technique for analyzing classification model performance using scikit-learn. You will also learn how to optimize classification and regression models through the use of hyperparameter tuning.

Exercise 1: How good is your model?Exercise 2: Deciding on a primary metric Exercise 3: Assessing a diabetes prediction classifier Exercise 4: Logistic regression and the ROC curve Exercise 5: Building a logistic regression model Exercise 6: The ROC curve Exercise 7: ROC AUC Exercise 8: Hyperparameter tuning Exercise 9: Hyperparameter tuning with GridSearchCV Exercise 10: Hyperparameter tuning with RandomizedSearchCV

Learn how to impute missing values, convert categorical data to numeric values, scale data, evaluate multiple supervised learning models simultaneously, and build pipelines to streamline your workflow!

Exercise 1: Preprocessing data Exercise 2: Creating dummy variables Exercise 3: Regression with categorical features Exercise 4: Handling missing data Exercise 5: Dropping missing data Exercise 6: Pipeline for song genre prediction: I Exercise 7: Pipeline for song genre prediction: II Exercise 8: Centering and scaling Exercise 9: Centering and scaling for regression Exercise 10: Centering and scaling for classification Exercise 11: Evaluating multiple models Exercise 12: Visualizing regression model performance Exercise 13: Predicting on the test set Exercise 14: Visualizing classification model performance Exercise 15: Pipeline for predicting song popularity Exercise 16: Congratulations