AdaBoost algorithm
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.
Install Sklearn
# 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% testBuilding 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)Done!
For more tutorial and details about Adaboost please follow official Sklearn Adaboost web page.
For latest Data science and Machine learning follow these links.
- This AI vision startup, Recogni, has its sights set on enabling fully autonomous vehicles
- Google AI Team Open Sources BiT – Big Transfer: General Visual Representation Learning (Computer Vision)
- Spiros Margaris of Margaris Ventures starts his first podcast interview series with Georgie Smallwood, N26’s Chief Product Officer
- BindsNET: An Open-Source Library Used For Simulating Spiking Neural Networks (SNNs) On CPUs Or GPUs Using PyTorch
- Introducing HANet: An Add-On Module For Urban-Scene Segmentation
