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

## Challenge: 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.

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.

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Section 3. Chapter 4

Course Content

Classification with Python

## Challenge: 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.

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.