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Classification with Python
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Зміст курсу

Classification with Python

Classification with Python

1. k-NN Classifier
What is ClassificationWhat is k-NNk-NN with Multiple FeaturesImplementing k-NNMulti-Class ClassificationTrain-test Split. Cross ValidationChallenge: Choosing the Best K Value.k-NN Summary
2. Logistic Regression
What is Logistic RegressionFinding the ParametersChallenge: Implementing Logistic RegressionDecision BoundaryOverfitting. RegularizationChallenge: Classifying Unseparateble DataLogistic Regression Summary
3. Decision Tree
What is Decision TreeSplitting the NodesCrazy TreeChallenge: Implementing a Decision TreeDecision Tree Summary
4. Random Forest
What is Random ForestThe Randomness of ForestChallenge: Implementing a Random ForestRandom Forest Summary
5. Comparing Models
Confusion MatrixMetricsChallenge: Comparing ModelsSummary

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())
copy

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

  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.

Завдання

  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.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Секція 3. Розділ 4
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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())
copy

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

  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.

Завдання

  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.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Секція 3. Розділ 4
toggle bottom row

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())
copy

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

  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.

Завдання

  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.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

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())
copy

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

  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.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Секція 3. Розділ 4
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
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