Add files via upload

Author: asRot0

model_selection_and_tuning.py

@@ -0,0 +1,100 @@
+"""
+Model Selection and Hyperparameter Tuning
+==========================================
+This script demonstrates how to select the best model for your dataset
+and optimize hyperparameters using GridSearchCV and RandomizedSearchCV.
+"""
+
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV
+from sklearn.metrics import accuracy_score
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.svm import SVC
+import numpy as np
+
+# Load Example Dataset
+data = load_iris()
+X, y = data.data, data.target
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+print(f"Training Samples: {X_train.shape[0]}, Test Samples: {X_test.shape[0]}")
+
+# 1. Model Selection: Comparing Different Models
+models = {
+    "Random Forest": RandomForestClassifier(random_state=42),
+    "SVM": SVC(random_state=42)
+}
+
+for name, model in models.items():
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    acc = accuracy_score(y_test, predictions)
+    print(f"{name} Accuracy: {acc:.4f}")
+
+# 2. Hyperparameter Tuning with GridSearchCV
+param_grid_rf = {
+    'n_estimators': [50, 100, 150],
+    'max_depth': [3, 5, 10, None],
+    'min_samples_split': [2, 5, 10]
+}
+
+print("\nPerforming GridSearchCV for Random Forest...")
+grid_search_rf = GridSearchCV(
+    RandomForestClassifier(random_state=42),
+    param_grid_rf,
+    cv=3,
+    scoring='accuracy',
+    n_jobs=-1
+)
+grid_search_rf.fit(X_train, y_train)
+
+print("Best Parameters for Random Forest:", grid_search_rf.best_params_)
+print("Best Cross-Validated Accuracy (RF):", grid_search_rf.best_score_)
+
+# 3. Hyperparameter Tuning with RandomizedSearchCV
+param_dist_svc = {
+    'C': np.logspace(-3, 3, 7),
+    'gamma': np.logspace(-3, 3, 7),
+    'kernel': ['rbf', 'poly', 'sigmoid']
+}
+
+print("\nPerforming RandomizedSearchCV for SVM...")
+random_search_svc = RandomizedSearchCV(
+    SVC(random_state=42),
+    param_distributions=param_dist_svc,
+    n_iter=20,
+    cv=3,
+    scoring='accuracy',
+    n_jobs=-1,
+    random_state=42
+)
+random_search_svc.fit(X_train, y_train)
+
+print("Best Parameters for SVM:", random_search_svc.best_params_)
+print("Best Cross-Validated Accuracy (SVM):", random_search_svc.best_score_)
+
+# 4. Final Evaluation with Best Models
+print("\nEvaluating Best Models on Test Data...")
+
+best_rf = grid_search_rf.best_estimator_
+best_svc = random_search_svc.best_estimator_
+
+rf_test_acc = accuracy_score(y_test, best_rf.predict(X_test))
+svc_test_acc = accuracy_score(y_test, best_svc.predict(X_test))
+
+print(f"Final Test Accuracy (Random Forest): {rf_test_acc:.4f}")
+print(f"Final Test Accuracy (SVM): {svc_test_acc:.4f}")
+
+# 5. Choosing the Best Model
+if rf_test_acc > svc_test_acc:
+    print("\nThe Best Model for This Dataset is Random Forest.")
+else:
+    print("\nThe Best Model for This Dataset is SVM.")
+
+'''
+Model Selection: Compares two popular models, Random Forest and SVM, to find the one with the best baseline accuracy.
+GridSearchCV: Systematic search over a predefined grid of hyperparameters for Random Forest.
+RandomizedSearchCV: Randomized search over a wide range of hyperparameters for SVM.
+Final Evaluation: Uses the best hyperparameters to evaluate performance on unseen test data.
+Best Model Selection: Selects the model with the highest test accuracy.
+'''