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Challenge: Implementing a Decision Tree | Decision Tree
Classification with Python

Challenge: Implementing a Decision TreeChallenge: Implementing a Decision Tree

In this challenge, you will use the titanic dataset. It holds information about passengers on the Titanic, including their age, sex, family size, etc. And the task is to predict whether a person survived or not.

To implement the Decision Tree, you can use the DecisionTreeClassifier from the sklearn.

Your task is to build a Decision Tree and find the best max_depth and min_samples_leaf using grid search.

Task

  1. Import the DecisionTreeClassifier class from sklearn.tree.
  2. Assign an instance of DecisionTreeClassifier to the decision_tree variable.
  3. Create a dictionary for a GridSearchCV to run through [1, 2, 3, 4, 5, 6, 7] values of max_depth and [1, 2, 4, 6] values of min_samples_leaf.
  4. Create a GridSearchCV object and train it.

Everything was clear?

Section 3. Chapter 4
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course content

Course Content

Classification with Python

Challenge: Implementing a Decision TreeChallenge: Implementing a Decision Tree

In this challenge, you will use the titanic dataset. It holds information about passengers on the Titanic, including their age, sex, family size, etc. And the task is to predict whether a person survived or not.

To implement the Decision Tree, you can use the DecisionTreeClassifier from the sklearn.

Your task is to build a Decision Tree and find the best max_depth and min_samples_leaf using grid search.

Task

  1. Import the DecisionTreeClassifier class from sklearn.tree.
  2. Assign an instance of DecisionTreeClassifier to the decision_tree variable.
  3. Create a dictionary for a GridSearchCV to run through [1, 2, 3, 4, 5, 6, 7] values of max_depth and [1, 2, 4, 6] values of min_samples_leaf.
  4. Create a GridSearchCV object and train it.

Everything was clear?

Section 3. Chapter 4
toggle bottom row
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