Predicting and hyperparameters tuning
Support Vector Machines (SVMs) with Scikit-learn

## Support Vector Machines (SVMs)¶

Introduction

• Support vector machines are models that learn to differentiate between data in two categories based on past examples
• We want to have the maximum margin from the line to the points as shown in the diagram and that is the essence of SVMs
• The points close to the decision boundary matters, the rest are not important
• Note that SVMs aim to classify correctly before maximizing the margin
• • You can also project your data into a higher dimensionality and split them with a hyperplane
• Optimization problem for finding maximum margins uses quadratic programming
• We use a kernel to identify similarity amongst points
• All kernels must satisfy Mercer conditions

SVM using Scikit-Learn

In :
from sklearn.svm import SVC
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score
import seaborn as sns

In :
# Create object

# Create data
X = iris.data
y = iris.target

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

In :
# Same 3 steps

# 1. Instantiate
# Default kernel='rbf'
# We can change to others
svm = SVC(kernel='linear')

# 2. Fit
svm.fit(X_train, y_train)

# 3. Predict
y_pred = svm.predict(X_test)

In :
# Accuracy calculation
acc = accuracy_score(y_pred, y_test)
acc

Out:
0.97368421052631582

How about data that do not seem linearly separable?

• We add a non-linear feature makes SVMs capable of linearly separating the data
• As you can see here, it seems that we cannot draw a linear line to split the data
• • However, we can add a new feature |x| to split the data
• The new points will be linearly separable
• • On the original plot, we can see this is how the data is separated
• • It seems like we need to create new features, but there's a cool trick called the "kernel trick"

Kernel Trick

• We can map non-separable data to a higher dimensionality to make it separable and then map back
• Hyperparameters Tuning with Scikit-Learn

• Kernels
• You can use common kernels and custom kernels
• Common kernels
• 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'
• C
• Controls tradeoff between smooth decision boundary and classifying training points correctly
• The C parameter tells the SVM optimization how much you want to avoid misclassifying each training example
• Large C
• The optimizer will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly
• Small C
• The optimizer will look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points
• For very tiny values of C, you should get misclassified examples, often even if your training data is linearly separable
• gamma
• Defines how far the influence of a single example reaches
• Low values
• Far
• Only consider close points
• High values
• Low
• Even far-away points get considered
• You can end up with a wiggly decision boundary (over-fitting)
• Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. If gamma is ‘auto’ then 1/n_features will be used instead.

SVMs Suitability

• Does not work well with large datasets due to speed issues
• Does not work well with a lot of noise
• Naive Bayes would be better
• Works well for data that can be linearly classified
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