Using Ensembles As Base Models

Utilizing ensemble models as base models within a stacking ensemble framework is a sophisticated approach in machine learning that offers several advantages and disadvantages. Here, we delve into the details of this method.

Pros of Using Ensembles as Base Models in Stacking Ensembles:

Enhanced Predictive Performance: Ensemble models, such as Random Forests, Gradient Boosting, or AdaBoost, are renowned for their capacity to enhance predictive accuracy. By integrating ensemble models as base learners, stacking can harness the collective predictive power of these models, often resulting in superior performance compared to individual models;
Diverse Modeling Approaches: Ensembles introduce diversity into the base models of a stacking ensemble. Different ensemble techniques have distinct strengths and weaknesses. Combining them broadens the range of modeling approaches employed, making the ensemble more adept at handling diverse data patterns and complexities;
Robustness Against Overfitting: Stacking with ensemble models can mitigate overfitting. Ensembles excel at reducing the impact of noise or outliers in the data, thereby enhancing the stacking ensemble's ability to generalize well to unseen data.

Cons of Using Ensembles as Base Models in Stacking Ensembles:

Increased Model Complexity: Employing ensemble models as base learners introduces complexity to the stacking ensemble. Managing multiple ensemble models necessitates careful consideration of hyperparameter tuning, training times, and computational resources;
Computational Overhead: Ensembles, especially deep or large ensembles, can be computationally intensive to train and evaluate. This can result in longer training times and may not be suitable for real-time or resource-constrained applications;
Interpretability Concerns: Ensembles, particularly deep ensembles, are frequently considered less interpretable than individual models. This reduced interpretability can hinder understanding model predictions, potentially limiting the model's utility in certain domains where interpretability is crucial.

Example

We will use the 'steel-plates-fault' dataset and try to create Stacking Classificator with ensembles as base models:

from sklearn.datasets import fetch_openml

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier

from sklearn.neural_network import MLPClassifier

from sklearn.ensemble import StackingClassifier

from sklearn.metrics import f1_score

import warnings

warnings.filterwarnings('ignore')

# Step 1: Load the dataset using fetch_openml

dataset = fetch_openml("steel-plates-fault", version=1)

# Extract features (X) and target variable (y)

X = dataset.data

y = dataset.target

# Step 2: Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 3: Create base models

rf_model = RandomForestClassifier(n_estimators=100, random_state=42)

ada_model = AdaBoostClassifier(n_estimators=50, random_state=42)

# Step 4: Create the meta model (neural network)

meta_model = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=500)

# Step 5: Create the stacking classifier

base_models = [('rf', rf_model), ('ada', ada_model)]

stacking_classifier = StackingClassifier(estimators=base_models, final_estimator=meta_model)

# Step 6: Train the stacking classifier

stacking_classifier.fit(X_train, y_train)

# Step 7: Make predictions

y_pred = stacking_classifier.predict(X_test)


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from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import f1_score
import warnings
warnings.filterwarnings('ignore')

# Step 1: Load the dataset using fetch_openml
dataset = fetch_openml("steel-plates-fault", version=1)

# Extract features (X) and target variable (y)
X = dataset.data
y = dataset.target

# Step 2: Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 3: Create base models
rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
ada_model = AdaBoostClassifier(n_estimators=50, random_state=42)

# Step 4: Create the meta model (neural network)
meta_model = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=500)

# Step 5: Create the stacking classifier
base_models = [('rf', rf_model), ('ada', ada_model)]
stacking_classifier = StackingClassifier(estimators=base_models, final_estimator=meta_model)

# Step 6: Train the stacking classifier
stacking_classifier.fit(X_train, y_train)

# Step 7: Make predictions
y_pred = stacking_classifier.predict(X_test)

# Step 8: Evaluate the results using F1 score
f1 = f1_score(y_test, y_pred, average='weighted')
print('F1 Score:', f1)

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Seksjon 4. Kapittel 4