Challenge: Solving Task Using Regularisation

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Your task is to create a classification model using L2 regularization on the breast_cancer dataset. It contains features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. The task associated with this dataset is to classify the breast mass as malignant (cancerous) or benign (non-cancerous) based on the extracted features.

Your task is to:

Specify argument at the LogisticRegression() constructor:
- specify penalty argument equal to l2;
- specify C argument equal to 1.
Fit regularized model on the training data.

Lösung

import numpy as np

import pandas as pd

from sklearn.datasets import load_breast_cancer

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import f1_score

import warnings

# Ignore warnings

warnings.filterwarnings("ignore")

# Load the Breast Cancer dataset as an example of a more complex classification dataset

data = load_breast_cancer()

X = data.data

y = 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 Logistic Regression model with L2 regularization (ridge)

logistic_reg = LogisticRegression(penalty="l2", C=1, random_state=42)

# Fit the model on the training data

logistic_reg.fit(X_train, y_train)

# Predict on the test data

y_pred = logistic_reg.predict(X_test)

# Calculate F1-score and display the confusion matrix

f1 = f1_score(y_test, y_pred)

print(f"F1-Score: {f1}")

War alles klar?

Danke für Ihr Feedback!

Abschnitt 3. Kapitel 4