I was going through a Udacity tutorial wherein a few data points were given and the exercise was to test which of the following models best fit the data: linear regression, decision tree, or SVM. Using sklearn, I was able to determine that that SVM is the best fit followed by decision tree. I got a very distinct decision boundary when these two algorithms were applied:
Is there any specific reason for the said shapes or does it just depend on the data sets?
The code was quite straightforward; just reading the CSV, separating the features and then applying the algorithms as shown below:
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.svm import SVC
import pandas
import numpy
# Read the data
data = pandas.read_csv('data.csv')
# Split the data into X and y
X = numpy.array(data[['x1', 'x2']])
y = numpy.array(data['y'])
# import statements for the classification algorithms
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.svm import SVC
# Logistic Regression Classifier
classifier = LogisticRegression()
classifier.fit(X,y)
# Decision Tree Classifier
classifier = GradientBoostingClassifier()
classifier.fit(X,y)
# Support Vector Machine Classifier
classifier = SVC()
classifier.fit(X,y)
