Improving Performance of ML Models

Performance Improvement with Ensembles

Ensembles can give us boost in the machine learning result by combining several models. Basically, ensemble models consist of several inspanidually 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 spanided into following two groups −

Sequential ensemble methods

As the name imppes, 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 imppes, 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 inspanidual 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 inspanidual 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 earper. The example of boosting method is AdaBoost.

Voting

In this ensemble learning model, multiple models of different types are built and some simple statistics, pke 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 vapdation as follows −

seed = 7
kfold = KFold(n_sppts=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 inspanidual 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 sppt point rather than greedily choosing the best sppt 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 vapdation as follows −

seed = 7
kfold = KFold(n_sppts=10, random_state=seed)

We need to provide the number of trees we are going to build. Here we are building 150 trees with sppt 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 vapdation as follows −

seed = 7
kfold = KFold(n_sppts=10, random_state=seed)

We need to provide the number of trees we are going to build. Here we are building 150 trees with sppt 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 vapdation as follows −

seed = 5
kfold = KFold(n_sppts=10, random_state=seed)

We need to provide the number of trees we are going to build. Here we are building 150 trees with sppt 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 vapdation as follows −

seed = 5
kfold = KFold(n_sppts=10, random_state=seed)

We need to provide the number of trees we are going to build. Here we are building 150 trees with sppt 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.pnear_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 vapdation as follows −

kfold = KFold(n_sppts=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.