Gradient Boosting و XGBoost

pip install xgboost

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
from xgboost import XGBClassifier

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 = XGBClassifier(
    n_estimators=300,       # number of trees
    learning_rate=0.05,     # how much each tree corrects (step size!)
    max_depth=3,            # small trees
    random_state=42,
)
model.fit(X_train, y_train)
print("F1 (churned):", round(f1_score(y_test, model.predict(X_test)), 3))

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
from xgboost import XGBClassifier

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"])

models = {
    "Logistic Regression": LogisticRegression(max_iter=1000),
    "Decision Tree": DecisionTreeClassifier(max_depth=4, random_state=42),
    "Random Forest": RandomForestClassifier(300, min_samples_leaf=5, random_state=42),
    "XGBoost": XGBClassifier(n_estimators=300, learning_rate=0.05,
                             max_depth=3, random_state=42),
}
for name, m in models.items():
    m.fit(X_train, y_train)
    auc = roc_auc_score(y_test, m.predict_proba(X_test)[:, 1])
    print(f"{name:20} AUC = {auc:.3f}")