Challenge: Solving Task Using Bagging Classifier

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The load_breast_cancer dataset is a built-in dataset provided by scikit-learn. It is commonly used for binary classification tasks, particularly in the context of breast cancer diagnosis. This dataset contains features that are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. The aim is to predict whether a given mass is malignant (cancerous) or benign (non-cancerous).

Your task is to solve the classification problem using BaggingClassifier on load_breast_cancer dataset:

Create an instance of BaggingClassifier class: specify base SVC (Support Vector Classifier) model and set the number of base estimators equal to 10.
Fit the ensemble model.
Get the final result using soft voting technique: for each sample in test dataset get the probability matrix and find the class with maximum probability.

Lösning

from sklearn.datasets import load_breast_cancer

from sklearn.model_selection import train_test_split

from sklearn.ensemble import BaggingClassifier

from sklearn.svm import SVC

from sklearn.metrics import f1_score

import numpy as np

# Load the Breast Cancer dataset

data = load_breast_cancer()

X, y = data.data, data.target

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a base model (SVM classifier)

base_model = SVC(kernel='rbf')

# Create the Bagging Classifier

bagging_model = BaggingClassifier(base_model, n_estimators=10, n_jobs=-1)

# Train the Bagging Classifier

bagging_model.fit(X_train, y_train)

# Make probability predictions on the test data

prob_predictions = bagging_model.predict_proba(X_test)

# Get final predictions by taking the class with maximum probability

predictions = [np.argmax(prob) for prob in prob_predictions]

# Calculate F1 score

f1 = f1_score(y_test, predictions)

print(f'F1 score: {f1:.4f}')

Var allt tydligt?

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Avsnitt 2. Kapitel 2