A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.

1. Support Vector Machines
(SVM)
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2. Support Vector Machines (SVM)
- Supervised learning methods
- Associated learning algorithms
- They can represent linear & non-linear functions and they have
an efficient training algorithm
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4. There are 2 kinds of SVM classifiers:
1.Linear SVM Classifier
2.Non-Linear SVM Classifier
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5. • Example:
Fig. 1 Fig. 2
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6. • Hyperplane
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7.  Example of Bad Decision Boundaries
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8.  Tuning Parameters
 Kernel
 Regularization
 Gamma
 Margin.
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9.  Kernel
linear kernel
f(x) = B(0) + sum(ai * (x,xi))
Polynomial kernel
K(x,xi) = 1 + sum(x * xi)^d
exponential Kernel
K(x,xi) = exp(-gamma * sum((x — xi²)).
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10.  Regularization
To avoid misclassifying each training example
low regularization value high regularization value
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11.  Gamma
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12.  Margin
separation of line to the closest class points.
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13. Problem Statement: Use Machine Learning to predict cases of breast cancer
using patient treatment history and health data.
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14. Dataset: Breast Cancer Wisconsin (Diagnostic)
Dataset
Let us have a quick look at the dataset
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15. Step 1: Load Pandas library and the dataset using Pandas
Step 2: Define the features and the target
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16. Have a look at the features:
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18. Step 3: Split the dataset into train and test using sklearn before building the SVM algorithm
model
Step 4: Import the support vector classifier function or SVC function from Sklearn SVM module. Build the Support
Vector Machine model with the help of the SVC function
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19. Step 5: Predict values using the SVM algorithm model
Step 6: Evaluate the Support Vector Machine model
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20.  SVM Applications:
• SVMS are a by product of Neural Network. They are widely applied to pattern
classification and regression problems. Here are some of its applications:
• Facial expression classification
• Speech recognition
• Handwritten digit recognition
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21.  Advantages of SVM Classifier:
 More effective
 It works effectively
 Memory efficient
 It is a robust model
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22.  Disadvantages of SVM Classifier:
• Not suitable
• It does not perform very well
• good generalization performance
• SVMs have high algorithmic complexity and extensive memory requirements due to the use of quadratic
programming.
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23. Thank You….
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