Challenge: Implementing Logistic Regression

Now let's implement the Logistic Regression in Python!
For this, the LogisticRegression class is used.

Note that by default, Logistic Regression uses the ℓ2 regularization (penalty='l2'). We will talk about regularization in later chapters. For now, we will stick to the default parameters.

The dataset for this chapter is about marketing campaigns based on phone calls from a Portuguese banking institution. The goal is to predict whether the user will subscribe to a term deposit.
The data is already preprocessed and ready to be fed to the model. Following chapters will cover the preprocessing needed for Logistic Regression.

Tarefa

Swipe to show code editor

Build a Logistic Regression model and calculate the accuracy on the training set.

Import LogisticRegression class.
Create an instance of class LogisticRegression with default parameters and train it.
Print the accuracy on the same X, y dataset.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 2. Capítulo 3

Challenge: Implementing Logistic Regression

Now let's implement the Logistic Regression in Python!
For this, the LogisticRegression class is used.

Note that by default, Logistic Regression uses the ℓ2 regularization (penalty='l2'). We will talk about regularization in later chapters. For now, we will stick to the default parameters.

Tarefa

Swipe to show code editor

Build a Logistic Regression model and calculate the accuracy on the training set.

Import LogisticRegression class.
Create an instance of class LogisticRegression with default parameters and train it.
Print the accuracy on the same X, y dataset.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 2. Capítulo 3

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo