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Classification with Python
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Classification with Python

Classification with Python

1. k-NN Classifier
What is ClassificationWhat is k-NNk-NN with Multiple FeaturesImplementing k-NNMulti-Class ClassificationTrain-test Split. Cross ValidationChallenge: Choosing the Best K Value.k-NN Summary
2. Logistic Regression
What is Logistic RegressionFinding the ParametersChallenge: Implementing Logistic RegressionDecision BoundaryOverfitting. RegularizationChallenge: Classifying Unseparateble DataLogistic Regression Summary
3. Decision Tree
What is Decision TreeSplitting the NodesCrazy TreeChallenge: Implementing a Decision TreeDecision Tree Summary
4. Random Forest
What is Random ForestThe Randomness of ForestChallenge: Implementing a Random ForestRandom Forest Summary
5. Comparing Models
Confusion MatrixMetricsChallenge: Comparing ModelsSummary

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

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

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

Tarefa

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

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

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Seção 2. Capítulo 3
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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

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

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

Tarefa

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

  1. Import LogisticRegression class.
  2. Create an instance of class LogisticRegression with default parameters and train it.
  3. 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?

Seção 2. Capítulo 3
toggle bottom row

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

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

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

Tarefa

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

  1. Import LogisticRegression class.
  2. Create an instance of class LogisticRegression with default parameters and train it.
  3. 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?

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

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

  1. Import LogisticRegression class.
  2. Create an instance of class LogisticRegression with default parameters and train it.
  3. 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
Seção 2. Capítulo 3
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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