البحث عن الـ Hyperparameters

import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, StratifiedKFold, train_test_split
from sklearn.pipeline import 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"])
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

pipe = Pipeline([("scale", StandardScaler()), ("model", LogisticRegression())])
search = GridSearchCV(pipe, {"model__C": [0.01, 0.03, 0.1, 0.3, 1, 3, 10]},
                      cv=cv, scoring="roc_auc")
search.fit(X_train, y_train)

results = pd.DataFrame(search.cv_results_)
print(results[["param_model__C", "mean_test_score", "std_test_score"]].round(3))
print("best:", search.best_params_, "CV AUC", round(search.best_score_, 3))

import pandas as pd
from sklearn.model_selection import GridSearchCV, StratifiedKFold, train_test_split
from sklearn.tree import DecisionTreeClassifier

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"])
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

grid = {"max_depth": [2, 3, 4, 5, None], "min_samples_leaf": [1, 5, 10, 20, 40]}
search = GridSearchCV(DecisionTreeClassifier(random_state=42), grid, cv=cv, scoring="roc_auc")
search.fit(X_train, y_train)

top = pd.DataFrame(search.cv_results_).sort_values("rank_test_score")
print(top[["param_max_depth", "param_min_samples_leaf", "mean_test_score"]].head(5).round(3))
print("worst:", round(top["mean_test_score"].min(), 3))
print("best:", search.best_params_, "| test AUC", round(search.score(X_test, y_test), 3))

import pandas as pd
from scipy.stats import randint
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV, StratifiedKFold, train_test_split

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"])
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

space = {"max_features": [1, 2, 3, 4, "sqrt"], "min_samples_leaf": randint(1, 30)}
search = RandomizedSearchCV(RandomForestClassifier(n_estimators=60, random_state=42), space,
                            n_iter=8, cv=cv, scoring="roc_auc", random_state=42)
search.fit(X_train, y_train)
print("best:", search.best_params_)
print("CV AUC", round(search.best_score_, 3), "| test AUC", round(search.score(X_test, y_test), 3))