تقييم الموديل صح

import numpy as np
from sklearn.metrics import accuracy_score, recall_score

y_true = np.array([1] * 50 + [0] * 9950)   # 50 frauds out of 10,000
y_lazy = np.zeros(10_000)                   # "model" that always says: not fraud

print(f"Accuracy: {accuracy_score(y_true, y_lazy):.1%}")
print(f"Frauds caught: {recall_score(y_true, y_lazy):.0%}")

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import ConfusionMatrixDisplay

df = pd.read_csv("churn.csv")
df["yearly"] = (df["contract"] == "yearly").astype(int)
features = ["tenure", "bill", "complaints", "data_gb", "yearly"]
X_train, X_test, y_train, y_test = train_test_split(
    df[features], df["churned"], test_size=0.2,
    random_state=42, stratify=df["churned"])
model = LogisticRegression(max_iter=1000).fit(X_train, y_train)

ConfusionMatrixDisplay.from_estimator(
    model, X_test, y_test, display_labels=["stayed", "churned"], cmap="Blues")
plt.title("Churn model on test set")
plt.show()

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

df = pd.read_csv("churn.csv")
df["yearly"] = (df["contract"] == "yearly").astype(int)
features = ["tenure", "bill", "complaints", "data_gb", "yearly"]
X_train, X_test, y_train, y_test = train_test_split(
    df[features], df["churned"], test_size=0.2,
    random_state=42, stratify=df["churned"])
model = LogisticRegression(max_iter=1000).fit(X_train, y_train)

print(classification_report(y_test, model.predict(X_test),
                            target_names=["stayed", "churned"]))