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Performance Improvement with Ensembles
Ensembles can give us boost in the machine learning result by combining several models. Basically, ensemble models consist of several individually trained supervised learning models and their results are merged in various ways to achieve better predictive performance compared to a single model. Ensemble methods can be divided into following two groups −
Sequential ensemble methods
As the name implies, in these kind of ensemble methods, the base learners are generated sequentially. The motivation of such methods is to exploit the dependency among base learners.
Parallel ensemble methods
As the name implies, in these kind of ensemble methods, the base learners are generated in parallel. The motivation of such methods is to exploit the independence among base learners.
Ensemble Learning Methods
The following are the most popular ensemble learning methods i.e. the methods for combining the predictions from different models −
Bagging
The term bagging is also known as bootstrap aggregation. In bagging methods, ensemble model tries to improve prediction accuracy and decrease model variance by combining predictions of individual models trained over randomly generated training samples. The final prediction of ensemble model will be given by calculating the average of all predictions from the individual estimators. One of the best examples of bagging methods are random forests.
Boosting
In boosting method, the main principle of building ensemble model is to build it incrementally by training each base model estimator sequentially. As the name suggests, it basically combine several week base learners, trained sequentially over multiple iterations of training data, to build powerful ensemble. During the training of week base learners, higher weights are assigned to those learners which were misclassified earlier. The example of boosting method is AdaBoost.
Voting
In this ensemble learning model, multiple models of different types are built and some simple statistics, like calculating mean or median etc., are used to combine the predictions. This prediction will serve as the additional input for training to make the final prediction.
Bagging Ensemble Algorithms
The following are three bagging ensemble algorithms −
Bagged Decision Tree
As we know that bagging ensemble methods work well with the algorithms that have high variance and, in this concern, the best one is decision tree algorithm. In the following Python recipe, we are going to build bagged decision tree ensemble model by using BaggingClassifier function of sklearn with DecisionTreeClasifier (a classification & regression trees algorithm) on Pima Indians diabetes dataset.
First, import the required packages as follows −
from pandas import read_csv from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score from sklearn.ensemble import BaggingClassifier from sklearn.tree import DecisionTreeClassifier
Now, we need to load the Pima diabetes dataset as we did in the previous examples −
path = r"C:pima-indians-diabetes.csv" headernames = [''preg'', ''plas'', ''pres'', ''skin'', ''test'', ''mass'', ''pedi'', ''age'', ''class''] data = read_csv(path, names=headernames) array = data.values X = array[:,0:8] Y = array[:,8]
Next, give the input for 10-fold cross validation as follows −
seed = 7 kfold = KFold(n_splits=10, random_state=seed) cart = DecisionTreeClassifier()
We need to provide the number of trees we are going to build. Here we are building 150 trees −
num_trees = 150
Next, build the model with the help of following script −
model = BaggingClassifier(base_estimator=cart, n_estimators=num_trees, random_state=seed)
Calculate and print the result as follows −
results = cross_val_score(model, X, Y, cv=kfold) print(results.mean())
Output
0.7733766233766234
The output above shows that we got around 77% accuracy of our bagged decision tree classifier model.
Random Forest
It is an extension of bagged decision trees. For individual classifiers, the samples of training dataset are taken with replacement, but the trees are constructed in such a way that reduces the correlation between them. Also, a random subset of features is considered to choose each split point rather than greedily choosing the best split point in construction of each tree.
In the following Python recipe, we are going to build bagged random forest ensemble model by using RandomForestClassifier class of sklearn on Pima Indians diabetes dataset.
First, import the required packages as follows −
from pandas import read_csv from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score from sklearn.ensemble import RandomForestClassifier
Now, we need to load the Pima diabetes dataset as did in previous examples −
path = r"C:pima-indians-diabetes.csv" headernames = [''preg'', ''plas'', ''pres'', ''skin'', ''test'', ''mass'', ''pedi'', ''age'', ''class''] data = read_csv(path, names=headernames) array = data.values X = array[:,0:8] Y = array[:,8]
Next, give the input for 10-fold cross validation as follows −
seed = 7 kfold = KFold(n_splits=10, random_state=seed)
We need to provide the number of trees we are going to build. Here we are building 150 trees with split points chosen from 5 features −
num_trees = 150 max_features = 5
Next, build the model with the help of following script −
model = RandomForestClassifier(n_estimators=num_trees, max_features=max_features)
Calculate and print the result as follows −
results = cross_val_score(model, X, Y, cv=kfold) print(results.mean())
Output
0.7629357484620642
The output above shows that we got around 76% accuracy of our bagged random forest classifier model.
Extra Trees
It is another extension of bagged decision tree ensemble method. In this method, the random trees are constructed from the samples of the training dataset.
In the following Python recipe, we are going to build extra tree ensemble model by using ExtraTreesClassifier class of sklearn on Pima Indians diabetes dataset.
First, import the required packages as follows −
from pandas import read_csv from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score from sklearn.ensemble import ExtraTreesClassifier
Now, we need to load the Pima diabetes dataset as did in previous examples −
path = r"C:pima-indians-diabetes.csv" headernames = [''preg'', ''plas'', ''pres'', ''skin'', ''test'', ''mass'', ''pedi'', ''age'', ''class''] data = read_csv(path, names=headernames) array = data.values X = array[:,0:8] Y = array[:,8]
Next, give the input for 10-fold cross validation as follows −
seed = 7 kfold = KFold(n_splits=10, random_state=seed)
We need to provide the number of trees we are going to build. Here we are building 150 trees with split points chosen from 5 features −
num_trees = 150 max_features = 5
Next, build the model with the help of following script −
model = ExtraTreesClassifier(n_estimators=num_trees, max_features=max_features)
Calculate and print the result as follows −
results = cross_val_score(model, X, Y, cv=kfold) print(results.mean())
Output
0.7551435406698566
The output above shows that we got around 75.5% accuracy of our bagged extra trees classifier model.
Boosting Ensemble Algorithms
The followings are the two most common boosting ensemble algorithms −
AdaBoost
It is one the most successful boosting ensemble algorithm. The main key of this algorithm is in the way they give weights to the instances in dataset. Due to this the algorithm needs to pay less attention to the instances while constructing subsequent models.
In the following Python recipe, we are going to build Ada Boost ensemble model for classification by using AdaBoostClassifier class of sklearn on Pima Indians diabetes dataset.
First, import the required packages as follows −
from pandas import read_csv from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score from sklearn.ensemble import AdaBoostClassifier
Now, we need to load the Pima diabetes dataset as did in previous examples −
path = r"C:pima-indians-diabetes.csv" headernames = [''preg'', ''plas'', ''pres'', ''skin'', ''test'', ''mass'', ''pedi'', ''age'', ''class''] data = read_csv(path, names=headernames) array = data.values X = array[:,0:8] Y = array[:,8]
Next, give the input for 10-fold cross validation as follows −
seed = 5 kfold = KFold(n_splits=10, random_state=seed)
We need to provide the number of trees we are going to build. Here we are building 150 trees with split points chosen from 5 features −
num_trees = 50
Next, build the model with the help of following script −
model = AdaBoostClassifier(n_estimators=num_trees, random_state=seed)
Calculate and print the result as follows −
results = cross_val_score(model, X, Y, cv=kfold) print(results.mean())
Output
0.7539473684210527
The output above shows that we got around 75% accuracy of our AdaBoost classifier ensemble model.
Stochastic Gradient Boosting
It is also called Gradient Boosting Machines. In the following Python recipe, we are going to build Stochastic Gradient Boostingensemble model for classification by using GradientBoostingClassifier class of sklearn on Pima Indians diabetes dataset.
First, import the required packages as follows −
from pandas import read_csv from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score from sklearn.ensemble import GradientBoostingClassifier
Now, we need to load the Pima diabetes dataset as did in previous examples −
path = r"C:pima-indians-diabetes.csv" headernames = [''preg'', ''plas'', ''pres'', ''skin'', ''test'', ''mass'', ''pedi'', ''age'', ''class''] data = read_csv(path, names=headernames) array = data.values X = array[:,0:8] Y = array[:,8]
Next, give the input for 10-fold cross validation as follows −
seed = 5 kfold = KFold(n_splits=10, random_state=seed)
We need to provide the number of trees we are going to build. Here we are building 150 trees with split points chosen from 5 features −
num_trees = 50
Next, build the model with the help of following script −
model = GradientBoostingClassifier(n_estimators=num_trees, random_state=seed)
Calculate and print the result as follows −
results = cross_val_score(model, X, Y, cv=kfold) print(results.mean())
Output
0.7746582365003418
The output above shows that we got around 77.5% accuracy of our Gradient Boosting classifier ensemble model.
Voting Ensemble Algorithms
As discussed, voting first creates two or more standalone models from training dataset and then a voting classifier will wrap the model along with taking the average of the predictions of sub-model whenever needed new data.
In the following Python recipe, we are going to build Voting ensemble model for classification by using VotingClassifier class of sklearn on Pima Indians diabetes dataset. We are combining the predictions of logistic regression, Decision Tree classifier and SVM together for a classification problem as follows −
First, import the required packages as follows −
from pandas import read_csv from sklearn.model_selection import KFold from sklearn.model_selection import cross_val_score from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.svm import SVC from sklearn.ensemble import VotingClassifier
Now, we need to load the Pima diabetes dataset as did in previous examples −
path = r"C:pima-indians-diabetes.csv" headernames = [''preg'', ''plas'', ''pres'', ''skin'', ''test'', ''mass'', ''pedi'', ''age'', ''class''] data = read_csv(path, names=headernames) array = data.values X = array[:,0:8] Y = array[:,8]
Next, give the input for 10-fold cross validation as follows −
kfold = KFold(n_splits=10, random_state=7)
Next, we need to create sub-models as follows −
estimators = []
model1 = LogisticRegression()
estimators.append((''logistic'', model1))
model2 = DecisionTreeClassifier()
estimators.append((''cart'', model2))
model3 = SVC()
estimators.append((''svm'', model3))
Now, create the voting ensemble model by combining the predictions of above created sub models.
ensemble = VotingClassifier(estimators) results = cross_val_score(ensemble, X, Y, cv=kfold) print(results.mean())
Output
0.7382262474367738
The output above shows that we got around 74% accuracy of our voting classifier ensemble model.
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