Machine Learning for Beginners in Python: Support Vector Classifier

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Support Vector Classifier

There is a balance between SVC maximizing the margin of the hyperplane and minimizing the misclassification. In SVC, the later is controlled with the hyperparameter , the penalty imposed on errors. C is a parameter of the SVC learner and is the penalty for misclassifying a data point. When C is small, the classifier is okay with misclassified data points (high bias but low variance). When C is large, the classifier is heavily penalized for misclassified data and therefore bends over backwards avoid any misclassified data points (low bias but high variance).

In scikit-learn, C is determined by the parameter C and defaults to C=1.0. We should treat C has a hyperparameter of our learning algorithm which we tune using model selection techniques.

Preliminaries


/* Load libraries */
from sklearn.svm import LinearSVC
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
import numpy as np

Load Iris Flower Data


/* Load feature and target data */
iris = datasets.load_iris()
X = iris.data
y = iris.target

Standardize Features


/* Standarize features */
scaler = StandardScaler()
X_std = scaler.fit_transform(X)

Train Support Vector Classifier


/* Create support vector classifier */
svc = LinearSVC(C=1.0)

/* Train model */
model = svc.fit(X_std, y)

Create Previously Unseen Observation


/* Create new observation */
new_observation = [[-0.7, 1.1, -1.1 , -1.7]]

Predict Class Of Observation


/* Predict class of new observation */
svc.predict(new_observation)

array([0])

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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