Boosting is a supervised machine learning algorithm for primarily handling data which have outlier and variance. Recently, boosting algorithms gained enormous popularity in data science. Boosting algorithms combine multiple low accuracy models to create a high accuracy model. AdaBoost is example of Boosting algorithm. The important advantages of AdaBoost Low generalization error, easy to implement, works with a wide range of classifiers, no parameters to adjust. Especial attention is needed to data as this algorithm is sensitive to outliers.
# For linux os
$ sudo pip install sklearn
Building Model in Python
Let’s first install the required Sklearn libraries in Python using pip.
from sklearn.ensemble import AdaBoostClassifier from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn import metrics
Loading iris dataset
There are 4 features (sepal length, sepal width, petal length, petal width) and a target four types of flower: Setosa, Versicolour, and Virginica.
iris = datasets.load_iris() X = iris.data y = iris.target print X.view
<built-in method view of numpy.ndarray object at 0x7f9b3e0d7df0> print X
[[5.1 3.5 1.4 0.2] [4.9 3. 1.4 0.2] [4.7 3.2 1.3 0.2] [4.6 3.1 1.5 0.2] ... [6.3 2.5 5. 1.9] [6.5 3. 5.2 2. ] [6.2 3.4 5.4 2.3] [5.9 3. 5.1 1.8]]
Split the data set
For better model training we would need Tesing and trainig sclices of the data.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) print "X_train:",len(X_train),"; X_test:",len(X_test),"; y_train:",len(y_train),"; y_test:",len(y_test)
X_train: 105 ; X_test: 45 ; y_train: 105 ; y_test: 4 70% training and 30% test
Building the Model, AdaBoost
Let’s build the AdaBoost Model using Scikit-learn using Decision Tree Classifier the default Classifier.
# Create adaboost object Adbc = AdaBoostClassifier(n_estimators=50, learning_rate=1.5) # Train Adaboost model = Adbc.fit(X_train, y_train) #Predict the response for test dataset y_pred = model.predict(X_test)
Evaluation of the model
print("Accuracy:",metrics.accuracy_score(y_test, y_pred)) #('Accuracy:', 0.8888888888888888)
For more tutorial and details about Adaboost please follow official Sklearn Adaboost web page.
For latest Data science and Machine learning follow these links.
- Researchers Use Unsupervised Machine Learning To Understand And Visualize The Evolution In Classical Music
- A Nepalese Machine Learning (ML) Researcher Introduces ‘Papers-With-Video’ Browser Extension Which Allows Users To Access Videos Related To Research Papers On ArXiv
- Facebook AI In Collaboration With NYU Introduce New Machine Learning (ML) Models To Predict COVID Patient’s Health Condition
- Google AI Introduces ToTTo: A Controlled Table-to-Text Generation Dataset Using Novel Annotation Process
- Model Proposed By Columbia University Can Learn Predictability From Unlabelled Video