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.


              1234
            
import 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.

Task

Swipe to show code editor

Import the DecisionTreeClassifier class from sklearn.tree.
Assign an instance of DecisionTreeClassifier to the decision_tree variable.
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.
Create a GridSearchCV object and train it.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 4

Challenge: Implementing a Decision Tree


              1234
            
import 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.

Task

Swipe to show code editor

Import the DecisionTreeClassifier class from sklearn.tree.
Assign an instance of DecisionTreeClassifier to the decision_tree variable.
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.
Create a GridSearchCV object and train it.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 4

Challenge: Implementing a Decision Tree


              1234
            
import 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.

Task

Swipe to show code editor

Import the DecisionTreeClassifier class from sklearn.tree.
Assign an instance of DecisionTreeClassifier to the decision_tree variable.
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.
Create a GridSearchCV object and train it.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!


              1234
            
import 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.

Task

Swipe to show code editor

Import the DecisionTreeClassifier class from sklearn.tree.
Assign an instance of DecisionTreeClassifier to the decision_tree variable.
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.
Create a GridSearchCV object and train it.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 3. Chapter 4

Switch to desktop for real-world practiceContinue from where you are using one of the options below