Developing an AdaBoost Ensemble in Python

Boosting is a class of ensemble machine learning algorithms that involve combining the predictions from many weak learners.

A weak learner is a model that is very simple, although has some skill on the dataset. Boosting was a theoretical concept long before a practical algorithm could be developed, and the AdaBoost (adaptive boosting) algorithm was the first successful approach for the idea.

The AdaBoost algorithm involves using very short (one-level) decision trees as weak learners that are added sequentially to the ensemble. Each subsequent model attempts to correct the predictions made by the model before it in the sequence. This is achieved by weighing the training dataset to put more focus on training examples on which prior models made prediction errors.

In this tutorial, you will discover how to develop AdaBoost ensembles for classification and regression.

After completing this tutorial, you will know:

• AdaBoost ensemble is an ensemble created from decision trees added sequentially to the model
• How to use the AdaBoost ensemble for classification and regression with scikit-learn.
• How to explore the effect of AdaBoost model hyperparameters on model performance.

Let’s get started.

Tutorial Overview

This tutorial is divided into four parts; they are:

1. AdaBoost Ensemble Algorithm
2. AdaBoost Scikit-Learn API
   1. AdaBoost for Classification
   2. AdaBoost for Regression
3. AdaBoost Hyperparameters
   1. Explore Number of Trees
   2. Explore Weak Learner
   3. Explore Learning Rate
   4. Explore Alternate Algorithm
4. Grid Search AdaBoost Hyperparameters

AdaBoost Ensemble Algorithm

Boosting refers to a class of machine learning ensemble algorithms where models are added sequentially and later models in the sequence correct the predictions made by earlier models in the sequence.

AdaBoost, short for “Adaptive Boosting,” is a boosting ensemble machine learning algorithm, and was one of the first successful boosting approaches.

We call the algorithm AdaBoost because, unlike previous algorithms, it adjusts adaptively to the errors of the weak hypotheses

— A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting, 1996.

AdaBoost combines the predictions from short one-level decision trees, called decision stumps, although other algorithms can also be used. Decision stump algorithms are used as the AdaBoost algorithm seeks to use many weak models and correct their predictions by adding additional weak models.

The training algorithm involves starting with one decision tree, finding those examples in the training dataset that were misclassified, and adding more weight to those examples. Another tree is trained on the same data, although now weighted by the misclassification errors. This process is repeated until a desired number of trees are added.

If a training data point is misclassified, the weight of that training data point is increased (boosted). A second classifier is built using the new weights, which are no longer equal. Again, misclassified training data have their weights boosted and the procedure is repeated.

— Multi-class AdaBoost, 2009.

The algorithm was developed for classification and involves combining the predictions made by all decision trees in the ensemble. A similar approach was also developed for regression problems where predictions are made by using the average of the decision trees. The contribution of each model to the ensemble prediction is weighted based on the performance of the model on the training dataset.

… the new algorithm needs no prior knowledge of the accuracies of the weak hypotheses. Rather, it adapts to these accuracies and generates a weighted majority hypothesis in which the weight of each weak hypothesis is a function of its accuracy.

— A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting, 1996.

Now that we are familiar with the AdaBoost algorithm, let’s look at how we can fit AdaBoost models in Python.

AdaBoost Scikit-Learn API

AdaBoost ensembles can be implemented from scratch, although this can be challenging for beginners.

For an example, see the tutorial:

• Boosting and AdaBoost for Machine Learning

The scikit-learn Python machine learning library provides an implementation of AdaBoost ensembles for machine learning.

It is available in a modern version of the library.

First, confirm that you are using a modern version of the library by running the following script:

# check scikit-learn version
import sklearn
print(sklearn.__version__)

Running the script will print your version of scikit-learn.

Your version should be the same or higher. If not, you must upgrade your version of the scikit-learn library.

0.22.1

AdaBoost is provided via the AdaBoostRegressor and AdaBoostClassifier classes.

Both models operate the same way and take the same arguments that influence how the decision trees are created.

Randomness is used in the construction of the model. This means that each time the algorithm is run on the same data, it will produce a slightly different model.

When using machine learning algorithms that have a stochastic learning algorithm, it is good practice to evaluate them by averaging their performance across multiple runs or repeats of cross-validation. When fitting a final model it may be desirable to either increase the number of trees until the variance of the model is reduced across repeated evaluations, or to fit multiple final models and average their predictions.

Let’s take a look at how to develop an AdaBoost ensemble for both classification and regression.

AdaBoost for Classification

In this section, we will look at using AdaBoost for a classification problem.

First, we can use the make_classification() function to create a synthetic binary classification problem with 1,000 examples and 20 input features.

The complete example is listed below.

# test classification dataset
from sklearn.datasets import make_classification
# define dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=6)
# summarize the dataset
print(X.shape, y.shape)

Running the example creates the dataset and summarizes the shape of the input and output components.

(1000, 20) (1000,)

Next, we can evaluate an AdaBoost algorithm on this dataset.

We will evaluate the model using repeated stratified k-fold cross-validation, with three repeats and 10 folds. We will report the mean and standard deviation of the accuracy of the model across all repeats and folds.

# evaluate adaboost algorithm for classification
from numpy import mean
from numpy import std
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.ensemble import AdaBoostClassifier
# define dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=6)
# define the model
model = AdaBoostClassifier()
# evaluate the model
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
n_scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1, error_score='raise')
# report performance
print('Accuracy: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))

Running the example reports the mean and standard deviation accuracy of the model.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

In this case, we can see the AdaBoost ensemble with default hyperparameters achieves a classification accuracy of about 80 percent on this test dataset.

Accuracy: 0.806 (0.041)

We can also use the AdaBoost model as a final model and make predictions for classification.

First, the AdaBoost ensemble is fit on all available data, then the predict() function can be called to make predictions on new data.

The example below demonstrates this on our binary classification dataset.

# make predictions using adaboost for classification
from sklearn.datasets import make_classification
from sklearn.ensemble import AdaBoostClassifier
# define dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=6)
# define the model
model = AdaBoostClassifier()
# fit the model on the whole dataset
model.fit(X, y)
# make a single prediction
row = [[-3.47224758,1.95378146,0.04875169,-0.91592588,-3.54022468,1.96405547,-7.72564954,-2.64787168,-1.81726906,-1.67104974,2.33762043,-4.30273117,0.4839841,-1.28253034,-10.6704077,-0.7641103,-3.58493721,2.07283886,0.08385173,0.91461126]]
yhat = model.predict(row)
print('Predicted Class: %d' % yhat[0])

Running the example fits the AdaBoost ensemble model on the entire dataset and is then used to make a prediction on a new row of data, as we might when using the model in an application.

Predicted Class: 0

Now that we are familiar with using AdaBoost for classification, let’s look at the API for regression.

AdaBoost for Regression

In this section, we will look at using AdaBoost for a regression problem.

First, we can use the make_regression() function to create a synthetic regression problem with 1,000 examples and 20 input features.

The complete example is listed below.

# test regression dataset
from sklearn.datasets import make_regression
# define dataset
X, y = make_regression(n_samples=1000, n_features=20, n_informative=15, noise=0.1, random_state=6)
# summarize the dataset
print(X.shape, y.shape)

(1000, 20) (1000,)

# evaluate adaboost ensemble for regression
from numpy import mean
from numpy import std
from sklearn.datasets import make_regression
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedKFold
from sklearn.ensemble import AdaBoostRegressor
# define dataset
X, y = make_regression(n_samples=1000, n_features=20, n_informative=15, noise=0.1, random_state=6)
# define the model
model = AdaBoostRegressor()
# evaluate the model
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
n_scores = cross_val_score(model, X, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1, error_score='raise')
# report performance
print('MAE: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))

MAE: -72.327 (4.041)

# adaboost ensemble for making predictions for regression
from sklearn.datasets import make_regression
from sklearn.ensemble import AdaBoostRegressor
# define dataset
X, y = make_regression(n_samples=1000, n_features=20, n_informative=15, noise=0.1, random_state=6)
# define the model
model = AdaBoostRegressor()
# fit the model on the whole dataset
model.fit(X, y)
# make a single prediction
row = [[1.20871625,0.88440466,-0.9030013,-0.22687731,-0.82940077,-1.14410988,1.26554256,-0.2842871,1.43929072,0.74250241,0.34035501,0.45363034,0.1778756,-1.75252881,-1.33337384,-1.50337215,-0.45099008,0.46160133,0.58385557,-1.79936198]]
yhat = model.predict(row)
print('Prediction: %d' % yhat[0])

Prediction: -10

# explore adaboost ensemble number of trees effect on performance
from numpy import mean
from numpy import std
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.ensemble import AdaBoostClassifier
from matplotlib import pyplot

# get the dataset
def get_dataset():
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=6)
return X, y

# get a list of models to evaluate
def get_models():
models = dict()
# define number of trees to consider
n_trees = [10, 50, 100, 500, 1000, 5000]
for n in n_trees:
models[str(n)] = AdaBoostClassifier(n_estimators=n)
return models

# evaluate a given model using cross-validation
def evaluate_model(model, X, y):
# define the evaluation procedure
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
# evaluate the model and collect the results
scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)
return scores

# define dataset
X, y = get_dataset()
# get the models to evaluate
models = get_models()
# evaluate the models and store results
results, names = list(), list()
for name, model in models.items():
# evaluate the model
scores = evaluate_model(model, X, y)
# store the results
results.append(scores)
names.append(name)
# summarize the performance along the way
print('>%s %.3f (%.3f)' % (name, mean(scores), std(scores)))
# plot model performance for comparison
pyplot.boxplot(results, labels=names, showmeans=True)
pyplot.show()

>10 0.773 (0.039)
>50 0.806 (0.041)
>100 0.801 (0.032)
>500 0.793 (0.028)
>1000 0.791 (0.032)
>5000 0.782 (0.031)

# explore adaboost ensemble tree depth effect on performance
from numpy import mean
from numpy import std
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from matplotlib import pyplot

# get the dataset
def get_dataset():
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=6)
return X, y

# get a list of models to evaluate
def get_models():
models = dict()
# explore depths from 1 to 10
for i in range(1,11):
# define base model
base = DecisionTreeClassifier(max_depth=i)
# define ensemble model
models[str(i)] = AdaBoostClassifier(base_estimator=base)
return models

# evaluate a given model using cross-validation
def evaluate_model(model, X, y):
# define the evaluation procedure
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
# evaluate the model and collect the results
scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)
return scores

# define dataset
X, y = get_dataset()
# get the models to evaluate
models = get_models()
# evaluate the models and store results
results, names = list(), list()
for name, model in models.items():
# evaluate the model
scores = evaluate_model(model, X, y)
# store the results
results.append(scores)
names.append(name)
# summarize the performance along the way
print('>%s %.3f (%.3f)' % (name, mean(scores), std(scores)))
# plot model performance for comparison
pyplot.boxplot(results, labels=names, showmeans=True)
pyplot.show()

Running the example first reports the mean accuracy for each configured weak learner tree depth.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

In this case, we can see that as the depth of the decision trees is increased, the performance of the ensemble is also increased on this dataset.

>1 0.806 (0.041)
>2 0.864 (0.028)
>3 0.867 (0.030)
>4 0.889 (0.029)
>5 0.909 (0.021)
>6 0.923 (0.020)
>7 0.927 (0.025)
>8 0.928 (0.028)
>9 0.923 (0.017)
>10 0.926 (0.030)

A box and whisker plot is created for the distribution of accuracy scores for each configured weak learner depth.

We can see the general trend of model performance and weak learner depth.

Explore Learning Rate

AdaBoost also supports a learning rate that controls the contribution of each model to the ensemble prediction.

This is controlled by the “learning_rate” argument and by default is set to 1.0 or full contribution. Smaller or larger values might be appropriate depending on the number of models used in the ensemble. There is a balance between the contribution of the models and the number of trees in the ensemble.

More trees may require a smaller learning rate; fewer trees may require a larger learning rate. It is common to use values between 0 and 1 and sometimes very small values to avoid overfitting such as 0.1, 0.01 or 0.001.

The example below explores learning rate values between 0.1 and 2.0 in 0.1 increments.

# explore adaboost ensemble learning rate effect on performance
from numpy import mean
from numpy import std
from numpy import arange
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.ensemble import AdaBoostClassifier
from matplotlib import pyplot

# get the dataset
def get_dataset():
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=6)
return X, y

# get a list of models to evaluate
def get_models():
models = dict()
# explore learning rates from 0.1 to 2 in 0.1 increments
for i in arange(0.1, 2.1, 0.1):
key = '%.3f' % i
models[key] = AdaBoostClassifier(learning_rate=i)
return models

# evaluate a given model using cross-validation
def evaluate_model(model, X, y):
# define the evaluation procedure
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
# evaluate the model and collect the results
scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)
return scores

# define dataset
X, y = get_dataset()
# get the models to evaluate
models = get_models()
# evaluate the models and store results
results, names = list(), list()
for name, model in models.items():
# evaluate the model
scores = evaluate_model(model, X, y)
# store the results
results.append(scores)
names.append(name)
# summarize the performance along the way
print('>%s %.3f (%.3f)' % (name, mean(scores), std(scores)))
# plot model performance for comparison
pyplot.boxplot(results, labels=names, showmeans=True)
pyplot.xticks(rotation=45)
pyplot.show()

Running the example first reports the mean accuracy for each configured learning rate.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

In this case, we can see similar values between 0.5 to 1.0 and a decrease in model performance after that.

>0.100 0.767 (0.049)
>0.200 0.786 (0.042)
>0.300 0.802 (0.040)
>0.400 0.798 (0.037)
>0.500 0.805 (0.042)
>0.600 0.795 (0.031)
>0.700 0.799 (0.035)
>0.800 0.801 (0.033)
>0.900 0.805 (0.032)
>1.000 0.806 (0.041)
>1.100 0.801 (0.037)
>1.200 0.800 (0.030)
>1.300 0.799 (0.041)
>1.400 0.793 (0.041)
>1.500 0.790 (0.040)
>1.600 0.775 (0.034)
>1.700 0.767 (0.054)
>1.800 0.768 (0.040)
>1.900 0.736 (0.047)
>2.000 0.682 (0.048)

A box and whisker plot is created for the distribution of accuracy scores for each configured learning rate.

We can see the general trend of decreasing model performance with a learning rate larger than 1.0 on this dataset.

Explore Alternate Algorithm

The default algorithm used in the ensemble is a decision tree, although other algorithms can be used.

The intent is to use very simple models, called weak learners. Also, the scikit-learn implementation requires that any models used must also support weighted samples, as they are how the ensemble is created by fitting models based on a weighted version of the training dataset.

The base model can be specified via the “base_estimator” argument. The base model must also support predicting probabilities or probability-like scores in the case of classification. If the specified model does not support a weighted training dataset, you will see an error message as follows:

ValueError: KNeighborsClassifier doesn't support sample_weight.

One example of a model that supports a weighted training is the logistic regression algorithm.

The example below demonstrates an AdaBoost algorithm with a LogisticRegression weak learner.

# evaluate adaboost algorithm with logistic regression weak learner for classification
from numpy import mean
from numpy import std
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.ensemble import AdaBoostClassifier
from sklearn.linear_model import LogisticRegression
# define dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=6)
# define the model
model = AdaBoostClassifier(base_estimator=LogisticRegression())
# evaluate the model
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
n_scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1, error_score='raise')
# report performance
print('Accuracy: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))

Running the example reports the mean and standard deviation accuracy of the model.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

In this case, we can see the AdaBoost ensemble with a logistic regression weak model achieves a classification accuracy of about 79 percent on this test dataset.

Accuracy: 0.794 (0.032)

Grid Search AdaBoost Hyperparameters

AdaBoost can be challenging to configure as the algorithm as many key hyperparameters that influence the behavior of the model on training data and the hyperparameters interact with each other.

As such, it is a good practice to use a search process to discover a configuration of the model hyperparameters that works well or best for a given predictive modeling problem. Popular search processes include a random search and a grid search.

In this section we will look at grid searching common ranges for the key hyperparameters for the AdaBoost algorithm that you can use as starting point for your own projects. This can be achieving using the GridSearchCV class and specifying a dictionary that maps model hyperparameter names to the values to search.

In this case, we will grid search two key hyperparameters for AdaBoost: the number of trees used in the ensemble and the learning rate. We will use a range of popular well performing values for each hyperparameter.

Each configuration combination will be evaluated using repeated k-fold cross-validation and configurations will be compared using the mean score, in this case, classification accuracy.

The complete example of grid searching the key hyperparameters of the AdaBoost algorithm on our synthetic classification dataset is listed below.

# example of grid searching key hyperparameters for adaboost on a classification dataset
from sklearn.datasets import make_classification
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import AdaBoostClassifier
# define dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=6)
# define the model with default hyperparameters
model = AdaBoostClassifier()
# define the grid of values to search
grid = dict()
grid['n_estimators'] = [10, 50, 100, 500]
grid['learning_rate'] = [0.0001, 0.001, 0.01, 0.1, 1.0]
# define the evaluation procedure
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
# define the grid search procedure
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=cv, scoring='accuracy')
# execute the grid search
grid_result = grid_search.fit(X, y)
# summarize the best score and configuration
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
# summarize all scores that were evaluated
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))

Running the example many take a while depending on your hardware. At the end of the run, the configuration that achieved the best score is reported first, followed by the scores for all other configurations that were considered.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

In this case, we can see that a configuration with 500 trees and a learning rate of 0.1 performed the best with a classification accuracy of about 81.3 percent.

The model may perform even better with more trees such as 1,000 or 5,000 although these configurations were not tested in this case to ensure that the grid search completed in a reasonable time.

Best: 0.813667 using {'learning_rate': 0.1, 'n_estimators': 500}
0.646333 (0.036376) with: {'learning_rate': 0.0001, 'n_estimators': 10}
0.646667 (0.036545) with: {'learning_rate': 0.0001, 'n_estimators': 50}
0.646667 (0.036545) with: {'learning_rate': 0.0001, 'n_estimators': 100}
0.647000 (0.038136) with: {'learning_rate': 0.0001, 'n_estimators': 500}
0.646667 (0.036545) with: {'learning_rate': 0.001, 'n_estimators': 10}
0.647000 (0.038136) with: {'learning_rate': 0.001, 'n_estimators': 50}
0.654333 (0.045511) with: {'learning_rate': 0.001, 'n_estimators': 100}
0.672667 (0.046543) with: {'learning_rate': 0.001, 'n_estimators': 500}
0.648333 (0.042197) with: {'learning_rate': 0.01, 'n_estimators': 10}
0.671667 (0.045613) with: {'learning_rate': 0.01, 'n_estimators': 50}
0.715000 (0.053213) with: {'learning_rate': 0.01, 'n_estimators': 100}
0.767667 (0.045948) with: {'learning_rate': 0.01, 'n_estimators': 500}
0.716667 (0.048876) with: {'learning_rate': 0.1, 'n_estimators': 10}
0.767000 (0.049271) with: {'learning_rate': 0.1, 'n_estimators': 50}
0.784667 (0.042874) with: {'learning_rate': 0.1, 'n_estimators': 100}
0.813667 (0.032092) with: {'learning_rate': 0.1, 'n_estimators': 500}
0.773333 (0.038759) with: {'learning_rate': 1.0, 'n_estimators': 10}
0.806333 (0.040701) with: {'learning_rate': 1.0, 'n_estimators': 50}
0.801000 (0.032491) with: {'learning_rate': 1.0, 'n_estimators': 100}
0.792667 (0.027560) with: {'learning_rate': 1.0, 'n_estimators': 500}

Summary

In this tutorial, you discovered how to develop AdaBoost ensembles for classification and regression.

Specifically, you learned:

• AdaBoost ensemble is an ensemble created from decision trees added sequentially to the model.
• How to use the AdaBoost ensemble for classification and regression with scikit-learn.
• How to explore the effect of AdaBoost model hyperparameters on model performance.

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