العتبات وعدم توازن الفئات

import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_score, recall_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

churn = pd.read_csv("churn.csv")
features = ["tenure_days", "orders", "total_spent", "avg_order_value",
            "days_since_last_order", "used_coupon", "support_tickets", "mobile_share"]
X_train, X_test, y_train, y_test = train_test_split(
    churn[features], churn["churned"], test_size=0.25, random_state=42,
    stratify=churn["churned"])
model = make_pipeline(StandardScaler(), LogisticRegression()).fit(X_train, y_train)
prob = model.predict_proba(X_test)[:, 1]

for t in [0.3, 0.4, 0.5, 0.6, 0.7, 0.8]:
    flagged = (prob >= t).astype(int)
    print(f"threshold {t:.1f}: flags {flagged.sum():2}  precision {precision_score(y_test, flagged):.2f}"
          f"  recall {recall_score(y_test, flagged):.2f}")

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import make_scorer
from sklearn.model_selection import TunedThresholdClassifierCV, train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

churn = pd.read_csv("churn.csv")
features = ["tenure_days", "orders", "total_spent", "avg_order_value",
            "days_since_last_order", "used_coupon", "support_tickets", "mobile_share"]
X_train, X_test, y_train, y_test = train_test_split(
    churn[features], churn["churned"], test_size=0.25, random_state=42,
    stratify=churn["churned"])

def profit(y_true, y_pred):
    y_true, y_pred = np.asarray(y_true), np.asarray(y_pred)
    tp = ((y_pred == 1) & (y_true == 1)).sum()
    fp = ((y_pred == 1) & (y_true == 0)).sum()
    return 10 * tp - 20 * fp

base = make_pipeline(StandardScaler(), LogisticRegression())
tuned = TunedThresholdClassifierCV(base, scoring=make_scorer(profit), cv=5).fit(X_train, y_train)
print("chosen threshold:", round(tuned.best_threshold_, 2))

default = base.fit(X_train, y_train)
print("test profit, contact everyone: ", profit(y_test, np.ones(len(y_test))))
print("test profit, threshold 0.5:    ", profit(y_test, default.predict(X_test)))
print("test profit, tuned threshold:  ", profit(y_test, tuned.predict(X_test)))

from sklearn.datasets import load_digits
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import average_precision_score, precision_score, recall_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

X, digit = load_digits(return_X_y=True)
y = (digit == 9).astype(int)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y)
print("share of 9s:", round(y.mean(), 3))

for weights in [None, "balanced"]:
    m = make_pipeline(StandardScaler(), LogisticRegression(class_weight=weights, max_iter=1000))
    m.fit(X_train, y_train)
    pred, prob = m.predict(X_test), m.predict_proba(X_test)[:, 1]
    print(f"class_weight={weights!s:9} accuracy {m.score(X_test, y_test):.3f}  "
          f"precision {precision_score(y_test, pred):.2f}  recall {recall_score(y_test, pred):.2f}  "
          f"avg precision {average_precision_score(y_test, prob):.3f}")