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.
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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())
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.
Compito
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.
Soluzione
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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_)
Tutto è chiaro?
Grazie per i tuoi commenti!
Sezione 3. Capitolo 4
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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_)
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