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.

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())


              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.

Завдання

Swipe to start coding

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.

Рішення

import pandas as pd

from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import GridSearchCV

# Read the data and assign the variables

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/titanic.csv')

X = df.drop(columns=['Survived'])

y = df['Survived']

decision_tree = DecisionTreeClassifier()

param_grid = {'max_depth': [1, 2, 3, 4, 5, 6, 7], 'min_samples_leaf': [1, 2, 4, 6]}

# Use `GridSearchCV` to find the best parameters

grid = GridSearchCV(decision_tree, param_grid, cv=10).fit(X, y)

# Print the best estimator and score

print(grid.best_estimator_)

print(grid.best_score_)

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 3. Розділ 4

single

import pandas as pd

from sklearn.tree import ___

from sklearn.model_selection import GridSearchCV

# Read the data and assign the variables

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/titanic.csv')

X = df.drop(columns=['Survived'])

y = df['Survived']

decision_tree = ___()

param_grid = {'max_depth': [1, 2, 3, 4, 5, 6, 7], '___': [1, 2, 4, 6]}

# Use `GridSearchCV` to find the best parameters

grid = GridSearchCV(decision_tree, param_grid, cv=10).___(X, y)

# Print the best estimator and score

print(grid.best_estimator_)

print(grid.best_score_)

Запитати АІ

Запитайте про що завгодно або спробуйте одне із запропонованих запитань, щоб почати наш чат