Model Hyperparameter Tuning

Model Hyperparameter Tuning

Overview

Hyperparameter tuning is the process of systematically searching for the best combination of model configuration parameters to maximize performance on validation data.

When to Use

• When optimizing model performance beyond baseline configurations
• When comparing different parameter combinations systematically
• When fine-tuning complex models with many hyperparameters
• When seeking the best trade-off between bias, variance, and training time
• When improving model generalization on validation and test data
• When exploring parameter spaces for neural networks, tree models, or ensemble methods

Tuning Methods

• Grid Search: Exhaustive search over parameter grid
• Random Search: Random sampling from parameter space
• Bayesian Optimization: Probabilistic model-based search
• Hyperband: Multi-fidelity optimization
• Evolutionary Algorithms: Genetic algorithm based search
• Population-based Training: Distributed parameter optimization

Hyperparameters by Model Type

• Tree Models: max_depth, min_samples_split, learning_rate
• Neural Networks: learning_rate, batch_size, num_layers, dropout
• SVM: C, kernel, gamma
• Ensemble: n_estimators, max_features, min_samples_leaf

Python Implementation

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
import optuna
from optuna.samplers import TPESampler
import torch
import torch.nn as nn
from torch.optim import Adam
import time

# Create dataset
X, y = make_classification(n_samples=2000, n_features=50, n_informative=30,
                          n_redundant=10, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

print("Dataset shapes:", X_train_scaled.shape, X_test_scaled.shape)

# 1. Grid Search
print("\n=== 1. Grid Search ===")
start = time.time()

param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [5, 10, 15],
    'min_samples_split': [2, 5, 10],
    'min_samples_leaf': [1, 2, 4]
}

grid_search = GridSearchCV(
    RandomForestClassifier(random_state=42),
    param_grid,
    cv=5,
    scoring='accuracy',
    n_jobs=-1,
    verbose=0
)

grid_search.fit(X_train_scaled, y_train)
grid_time = time.time() - start

print(f"Best parameters: {grid_search.best_params_}")
print(f"Best CV score: {grid_search.best_score_:.4f}")
print(f"Test score: {grid_search.score(X_test_scaled, y_test):.4f}")
print(f"Time taken: {grid_time:.2f}s")

# 2. Random Search
print("\n=== 2. Random Search ===")
start = time.time()

param_dist = {
    'n_estimators': np.arange(50, 300, 10),
    'max_depth': np.arange(5, 30, 1),
    'min_samples_split': np.arange(2, 20, 1),
    'min_samples_leaf': np.arange(1, 10, 1),
    'max_features': ['sqrt', 'log2']
}

random_search = RandomizedSearchCV(
    RandomForestClassifier(random_state=42),
    param_dist,
    n_iter=20,
    cv=5,
    scoring='accuracy',
    n_jobs=-1,
    random_state=42,
    verbose=0
)

random_search.fit(X_train_scaled, y_train)
random_time = time.time() - start

print(f"Best parameters: {random_search.best_params_}")
print(f"Best CV score: {random_search.best_score_:.4f}")
print(f"Test score: {random_search.score(X_test_scaled, y_test):.4f}")
print(f"Time taken: {random_time:.2f}s")

# 3. Bayesian Optimization with Optuna
print("\n=== 3. Bayesian Optimization (Optuna) ===")

def objective(trial):
    params = {
        'n_estimators': trial.suggest_int('n_estimators', 50, 300),
        'max_depth': trial.suggest_int('max_depth', 5, 30),
        'min_samples_split': trial.suggest_int('min_samples_split', 2, 20),
        'min_samples_leaf': trial.suggest_int('min_samples_leaf', 1, 10),
        'max_features': trial.suggest_categorical('max_features', ['sqrt', 'log2'])
    }

    model = RandomForestClassifier(**params, random_state=42)
    scores = cross_val_score(model, X_train_scaled, y_train, cv=5, scoring='accuracy')
    return scores.mean()

start = time.time()
sampler = TPESampler(seed=42)
study = optuna.create_study(sampler=sampler, direction='maximize')
study.optimize(objective, n_trials=20, show_progress_bar=False)
optuna_time = time.time() - start

best_trial = study.best_trial
print(f"Best parameters: {best_trial.params}")
print(f"Best CV score: {best_trial.value:.4f}")

# Train final model with best params
best_model = RandomForestClassifier(**best_trial.params, random_state=42)
best_model.fit(X_train_scaled, y_train)
print(f"Test score: {best_model.score(X_test_scaled, y_test):.4f}")
print(f"Time taken: {optuna_time:.2f}s")

# 4. Gradient Boosting hyperparameter tuning
print("\n=== 4. Gradient Boosting Tuning ===")

gb_param_grid = {
    'learning_rate': [0.01, 0.05, 0.1, 0.2],
    'n_estimators': [100, 200, 300],
    'max_depth': [3, 5, 7, 9],
    'min_samples_split': [2, 5, 10],
    'subsample': [0.8, 0.9, 1.0]
}

gb_search = GridSearchCV(
    GradientBoostingClassifier(random_state=42),
    gb_param_grid,
    cv=5,
    scoring='accuracy',
    n_jobs=-1,
    verbose=0
)

gb_search.fit(X_train_scaled, y_train)

print(f"Best parameters: {gb_search.best_params_}")
print(f"Best CV score: {gb_search.best_score_:.4f}")
print(f"Test score: {gb_search.score(X_test_scaled, y_test):.4f}")

# 5. Learning rate tuning for neural networks
print("\n=== 5. Learning Rate Tuning for Neural Networks ===")

class SimpleNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(50, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 1)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(0.3)

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.relu(self.fc2(x))
        x = self.dropout(x)
        x = torch.sigmoid(self.fc3(x))
        return x

learning_rates = [0.0001, 0.001, 0.01, 0.1]
lr_results = {}

device = torch.device('cpu')

for lr in learning_rates:
    model = SimpleNN().to(device)
    optimizer = Adam(model.parameters(), lr=lr)
    criterion = nn.BCELoss()

    X_train_tensor = torch.FloatTensor(X_train_scaled)
    y_train_tensor = torch.FloatTensor(y_train).unsqueeze(1)

    best_loss = float('inf')
    patience = 10
    patience_counter = 0

    for epoch in range(100):
        output = model(X_train_tensor)
        loss = criterion(output, y_train_tensor)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if loss.item() < best_loss:
            best_loss = loss.item()
            patience_counter = 0
        else:
            patience_counter += 1

        if patience_counter >= patience:
            break

    lr_results[lr] = best_loss
    print(f"Learning Rate {lr}: Best Loss = {best_loss:.6f}")

# 6. Comparison visualization
fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Search method comparison
methods = ['Grid Search', 'Random Search', 'Bayesian Opt']
times = [grid_time, random_time, optuna_time]
scores = [grid_search.best_score_, random_search.best_score_, study.best_value]

x = np.arange(len(methods))
axes[0, 0].bar(x, times, color='steelblue', alpha=0.7)
axes[0, 0].set_ylabel('Time (seconds)')
axes[0, 0].set_title('Tuning Method Comparison - Time')
axes[0, 0].set_xticks(x)
axes[0, 0].set_xticklabels(methods)

axes[0, 1].bar(x, scores, color='coral', alpha=0.7)
axes[0, 1].set_ylabel('CV Accuracy')
axes[0, 1].set_title('Tuning Method Comparison - Accuracy')
axes[0, 1].set_xticks(x)
axes[0, 1].set_xticklabels(methods)
axes[0, 1].set_ylim([0.8, 1.0])

# Hyperparameter importance from Optuna
importance_dict = {}
for param_name in study.best_trial.params.keys():
    trial_values = []
    for trial in study.trials:
        if param_name in trial.params:
            trial_values.append(trial.value)
    if trial_values:
        importance_dict[param_name] = np.std(trial_values)

axes[1, 0].barh(list(importance_dict.keys()), list(importance_dict.values()),
               color='lightgreen', edgecolor='black')
axes[1, 0].set_xlabel('Importance (Std Dev)')
axes[1, 0].set_title('Hyperparameter Importance')

# Learning rate tuning for NN
axes[1, 1].plot(list(lr_results.keys()), list(lr_results.values()), marker='o',
               linewidth=2, markersize=8, color='purple')
axes[1, 1].set_xlabel('Learning Rate')
axes[1, 1].set_ylabel('Best Training Loss')
axes[1, 1].set_title('Learning Rate Impact on Neural Network')
axes[1, 1].set_xscale('log')
axes[1, 1].grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('hyperparameter_tuning.png', dpi=100, bbox_inches='tight')
print("\nVisualization saved as 'hyperparameter_tuning.png'")

print("\nHyperparameter tuning completed!")

Tuning Strategy by Model

• Tree Models: Focus on depth, min_samples, max_features
• Boosting: Learning_rate, n_estimators, subsample
• Neural Networks: Learning rate, batch size, regularization
• SVM: C and kernel type are most important

Best Practices

• Scale search space logarithmically for continuous parameters
• Use cross-validation for robust estimates
• Start with random search for initial exploration
• Use Bayesian optimization for final refinement
• Monitor for diminishing returns

Deliverables

• Optimal hyperparameters found
• Performance metrics for top configurations
• Tuning efficiency analysis
• Visualization of parameter impact
• Tuning report and recommendations