Introduction

In this article, we will explore different techniques for finding the optimal hyperparameter values from a given set of parameters in a grid. Particularly we will look at RandomizedSearchCV, GridSearchCV, and BayesSearchCV.

In this blog you will learn:

How to initialize the parameter grid.
How to find the optimal hyperparameters based on a given technique.
How to build a model (XGBClassifier) to use the hyperparameters.
How to score the performance of the model.

RandomizedSearchCV

param_grid = {
    "gamma": [0, 0.1, 0.2, 0.5, 1, 1.5, 2, 3, 6, 12, 20],
    "learning_rate": [0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.3, 0.5, 0.7, 0.8],
    "max_depth": [1, 2, 3, 4, 5, 6, 8, 12],
    "n_estimators": [25, 50, 65, 80, 100, 115, 200]
}

grid_search = RandomizedSearchCV(estimator=classifier_0, param_distributions=param_grid, scoring=scoring)

GridSearchCV

param_grid = {
    "gamma": [0, 0.1, 0.2, 0.5, 1, 1.5, 2, 3, 6, 12, 20],
    "learning_rate": [0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.3, 0.5, 0.7, 0.8],
    "max_depth": [2, 3, 4, 5, 6, 8, 12],
    "n_estimators": [25, 50, 65, 80, 100, 115, 200]
}

grid_search = GridSearchCV(estimator=classifier_0, param_grid=param_grid, scoring=scoring)

BayesSearchCV

param_bayes = {
    'gamma': Categorical(param_grid['gamma']),
    'learning_rate': Categorical(param_grid['learning_rate']),
    'max_depth': Categorical(param_grid['max_depth']),
    'n_estimators': Categorical(param_grid['n_estimators'])
}

grid_search = BayesSearchCV(estimator=classifier_0, search_spaces=param_bayes, scoring=scoring, n_jobs=-1, cv=10)

Finding the Best HyperParameters

best_model = grid_search.fit(X_train, y_train)
hyperparams = best_model.best_params_

Building and Scoring the Classifier using the HyperParameters

# Fitting the Model
ne = hyperparams['n_estimators']
lr = hyperparams['learning_rate']
md = hyperparams['max_depth']
gm = hyperparams['gamma']
print("Recommended Params >>", f"ne: {ne},", f"lr: {lr}", f"md: {md}", f"gm: {gm}")

# Build Classification Model
classifier_1 = XGBClassifier(
    base_score=0.5,
    colsample_bylevel=1,
    colsample_bynode=1,
    objective=objective,
    booster="gbtree",
    eval_metric=eval_metric_list,
    n_estimators=ne,
    learning_rate=lr,
    max_depth=md,
    gamma=gm,
    subsample=0.8,
    colsample_bytree=1,
    random_state=1
)

# Fit Model
eval_set = [(X_train, y_train)]
classifier_1.fit(
    X_train,
    y_train,
    eval_set=eval_set,
    verbose=False
)

# Get predictions for training data
train_yhat = classifier_1.predict(X_train)
print("Training Preds: \n", train_yhat[:5])

# Set K-Fold Cross Validation Levels
cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=3, random_state=1)

# Training Results
train_results = cross_val_score(classifier_1, X_train, y_train, scoring=scoring, cv=cv, n_jobs=1)

# Brief Review of Training Results
print("Average Accuracy K-Fold: ", round(train_results.mean(), 2))
print("Std Deviation K-Fold: ", round(train_results.std(), 2))
print("Precision Score 0: ", round(precision_score(y_train, train_yhat, average=None)[0], 3))
print("Precision Score 1: ", round(precision_score(y_train, train_yhat, average=None)[1], 3))

Performance

Machine: Laptop
Processor: AMD Ryzen 7
OS: Windows
DataFrame Shape: (7282, 17)

Blog

Hands-On Coding: Exploring Hyperparameters for Programmers

Ed Legaspi