Support vector machines (SVMs) are one of the world's most popular machine learning problems.
SVMs can be used for either classification problems or regression problems, which makes them quite versatile.
In this tutorial, you will learn how to build your first Python support vector machines model from scratch using the breast cancer data set included with
You can skip to a specific section of this Python machine learning tutorial using the table of contents below:
- The Python Libraries We Will Need In This Tutorial
- The Data Set We Will Use In This Tutorial
- Splitting the Data Set Into Training Data and Test Data
- Training The Support Vector Machines Model
- Making Predictions With Our Support Vector Machines Model
- Assessing the Performance of Our Support Vector Machines Model
- The Full Code For This Tutorial
- Final Thoughts
import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline import seaborn as sns
Next up, you'll import the data set we will be using throughout this tutorial.
This tutorial makes use of the breast cancer data set that comes included with
scikit-learn. Accordingly, we will now import that data set into our Python script.
First, import the
load_breast_cancer function from the
datasets module of
scikit-learn with this command:
from sklearn.datasets import load_breast_cancer
Next, you need to create an instance of the breast cancer data set. The following statement should do the trick:
cancer_data = load_breast_cancer()
cancer_data variable includes more than just the breast cancer data set. As an example, we will see shortly that there is a useful description contained in this
raw_data data structure.
Because of this, the last step that we need to do in importing the data set is store data alone in its own DataFrame called
raw_data. Here is the code to do this:
raw_data = pd.DataFrame(cancer_data['data'], columns = cancer_data['feature_names'])
Let's investigate what's actually contained in this data set.
Every data set included in
scikit-learn comes with a description field that can help you understand what the data set is describing.
Let's print this description. The following statement should do the trick:
.. _breast_cancer_dataset: Breast cancer wisconsin (diagnostic) dataset -------------------------------------------- **Data Set Characteristics:** :Number of Instances: 569 :Number of Attributes: 30 numeric, predictive attributes and the class :Attribute Information: - radius (mean of distances from center to points on the perimeter) - texture (standard deviation of gray-scale values) - perimeter - area - smoothness (local variation in radius lengths) - compactness (perimeter^2 / area - 1.0) - concavity (severity of concave portions of the contour) - concave points (number of concave portions of the contour) - symmetry - fractal dimension ("coastline approximation" - 1) The mean, standard error, and "worst" or largest (mean of the three worst/largest values) of these features were computed for each image, resulting in 30 features. For instance, field 0 is Mean Radius, field 10 is Radius SE, field 20 is Worst Radius. - class: - WDBC-Malignant - WDBC-Benign :Summary Statistics: ===================================== ====== ====== Min Max ===================================== ====== ====== radius (mean): 6.981 28.11 texture (mean): 9.71 39.28 perimeter (mean): 43.79 188.5 area (mean): 143.5 2501.0 smoothness (mean): 0.053 0.163 compactness (mean): 0.019 0.345 concavity (mean): 0.0 0.427 concave points (mean): 0.0 0.201 symmetry (mean): 0.106 0.304 fractal dimension (mean): 0.05 0.097 radius (standard error): 0.112 2.873 texture (standard error): 0.36 4.885 perimeter (standard error): 0.757 21.98 area (standard error): 6.802 542.2 smoothness (standard error): 0.002 0.031 compactness (standard error): 0.002 0.135 concavity (standard error): 0.0 0.396 concave points (standard error): 0.0 0.053 symmetry (standard error): 0.008 0.079 fractal dimension (standard error): 0.001 0.03 radius (worst): 7.93 36.04 texture (worst): 12.02 49.54 perimeter (worst): 50.41 251.2 area (worst): 185.2 4254.0 smoothness (worst): 0.071 0.223 compactness (worst): 0.027 1.058 concavity (worst): 0.0 1.252 concave points (worst): 0.0 0.291 symmetry (worst): 0.156 0.664 fractal dimension (worst): 0.055 0.208 ===================================== ====== ====== :Missing Attribute Values: None :Class Distribution: 212 - Malignant, 357 - Benign :Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian :Donor: Nick Street :Date: November, 1995 This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets. https://goo.gl/U2Uwz2 Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes. The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34]. This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WDBC/ .. topic:: References - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science and Technology, volume 1905, pages 861-870, San Jose, CA, 1993. - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), pages 570-577, July-August 1995. - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) 163-171.
The most important takeaways from this data set description are:
- There are 569 observations in the data set
- Each observation has 30 numeric attributes
Now that we have an understanding of how our data set is structured, let's move on to splitting our data set into training data and test data.
To split our data set into training data and test data, the first thing we need to do is specify our
x variables will be the
raw_data pandas DataFrame that we created earlier. Our
y variables need to be parsed from the original
cancer_data object that we created earlier, where it is stored under the key
More specifically, here is the code to create our
x = raw_data y = cancer_data['target']
We will be using
train_test_split function combined with list unpacking to split our data set into training data and test data (just like we did with linear regression and logistic regression earlier in this course).
First you'll need to import the function with the following statement:
from sklearn.model_selection import train_test_split
Now you can create training data and test data along both the
y axes with the following statement:
x_training_data, x_test_data, y_training_data, y_test_data = train_test_split(x, y, test_size = 0.3)
This splits the data such that the test data is 30% of the original data set (indicated by the parameter
test_size = 0.3).
Now that our data is split, let's move on to training our first support vector machines model.
Before you can train your first support vector machine model, you'll need to import the model class from
SVC class lives within
svm module. Here is the statement to import it:
from sklearn.svm import SVC
Now let's create an instance of this class and assign it to the variable
model = SVC()
We can now train the SVM model using the same method as with our k-nearest neighbors model and our random forests model earlier in this course: by invoking the
fit method on it, and passing in
Here's the code to do this:
Our model has now been trained. Let's move on to making predictions with the model in the next section of this tutorial.
Any machine learning model created using
scikit-learn can be used to make predictions by simply invoking the
predict method on it and passing in the array of values that you'd like to generate predictions from.
In this case, here is the Python statement that you would use to store predictions from the
x_test_data in a variable called
predictions = model.predict(x_test_data)
We'll assess the performance of our model next.
We'll use the same performance measurement techniques for our support vector machines model as we did with the other classification models we've built in this course: a
classification_report and a
To start, let's import these functions from
from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix
First let's generate our classification_report:
precision recall f1-score support 0 1.00 0.84 0.91 67 1 0.90 1.00 0.95 104 accuracy 0.94 171 macro avg 0.95 0.92 0.93 171 weighted avg 0.94 0.94 0.93 171
Next let's generate our confusion matrix:
[[ 56 11] [ 0 104]]
You can view the full code for this tutorial in this GitHub repository. It is also pasted below for your reference:
#Data imports import pandas as pd import numpy as np #Visualization imports import matplotlib.pyplot as plt %matplotlib inline import seaborn as sns #Import the data set from scikit-learn from sklearn.datasets import load_breast_cancer cancer_data = load_breast_cancer() raw_data = pd.DataFrame(cancer_data['data'], columns = cancer_data['feature_names']) # print(cancer_data['DESCR']) #Split the data set into training data and test data x = raw_data y = cancer_data['target'] from sklearn.model_selection import train_test_split x_training_data, x_test_data, y_training_data, y_test_data = train_test_split(x, y, test_size = 0.3) #Train the SVM model from sklearn.svm import SVC model = SVC() model.fit(x_training_data, y_training_data) #Make predictions with the model predictions = model.predict(x_test_data) #Measure the performance of our model from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix print(classification_report(y_test_data, predictions)) print(confusion_matrix(y_test_data, predictions))
In this tutorial, you learned how to build Python support vector machines models.
Here is a brief summary of what was discussed in this tutorial:
- How to import and load the built-in breast cancer data set from
- How to print descriptions from the built-in datasets included with
- How to split your data set into training data and test data using
- How to import the
- How to train an SVM model
- How to make predictions with a support vector machines model in Python
- How to measure the performance of a support vector machines model using the