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.
Swipe to start coding
Build a Logistic Regression model and calculate the accuracy on the training set.
- Import
LogisticRegressionclass. - Create an instance of class
LogisticRegressionwith default parameters and train it. - Print the accuracy on the same
X, ydataset.
Solución
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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.
Swipe to start coding
Build a Logistic Regression model and calculate the accuracy on the training set.
- Import
LogisticRegressionclass. - Create an instance of class
LogisticRegressionwith default parameters and train it. - Print the accuracy on the same
X, ydataset.
Solución
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