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.
1234import pandas as pd df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/titanic.csv') print(df.head())
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.
Swipe to start coding
- Import the
DecisionTreeClassifierclass fromsklearn.tree. - Assign an instance of
DecisionTreeClassifierto thedecision_treevariable. - Create a dictionary for a
GridSearchCVto run through[1, 2, 3, 4, 5, 6, 7]values ofmax_depthand[1, 2, 4, 6]values ofmin_samples_leaf. - Create a
GridSearchCVobject and train it.
Oplossing
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Completion rate improved to 3.57Challenge: Implementing a Decision Tree
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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.
1234import pandas as pd df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/titanic.csv') print(df.head())
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.
Swipe to start coding
- Import the
DecisionTreeClassifierclass fromsklearn.tree. - Assign an instance of
DecisionTreeClassifierto thedecision_treevariable. - Create a dictionary for a
GridSearchCVto run through[1, 2, 3, 4, 5, 6, 7]values ofmax_depthand[1, 2, 4, 6]values ofmin_samples_leaf. - Create a
GridSearchCVobject and train it.
Oplossing
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Awesome!
Completion rate improved to 3.57single
