Create Test DataSets using Sklearn and Python
Python’s Sklearn library provides a great sample dataset generator which will help you to create your own custom dataset. It’s fast and very easy to use. Following are the types of samples it provides.
For all the above methods you need to import sklearn.datasets.samples_generator.
# importing libraries from sklearn.datasets.samples_generator # matplotlib for ploting from matplotlib import pyplot as plt from matplotlib import style |
sklearn.datasets.make_blobs
# Creating Test DataSets using sklearn.datasets.make_blobs from sklearn.datasets.samples_generator import make_blobs from matplotlib import pyplot as plt from matplotlib import style style.use("fivethirtyeight") X, y = make_blobs(n_samples = 100, centers = 3, cluster_std = 1, n_features = 2) plt.scatter(X[:, 0], X[:, 1], s = 40, color = 'g') plt.xlabel("X") plt.ylabel("Y") plt.show() plt.clf() |
Output:
make_blobs with 3 centers
sklearn.datasets.make_moon
# Creating Test DataSets using sklearn.datasets.make_moon from sklearn.datasets.samples_generator import make_moon from matplotlib import pyplot as plt from matplotlib import style X, y = make_moons(n_samples = 1000, noise = 0.1) plt.scatter(X[:, 0], X[:, 1], s = 40, color ='g') plt.xlabel("X") plt.ylabel("Y") plt.show() plt.clf() |
Output:
make_moons with 1000 data points
sklearn.datasets.make_circle
# Creating Test DataSets using sklearn.datasets.make_circles from sklearn.datasets.samples_generator import make_circles from matplotlib import pyplot as plt from matplotlib import style style.use("fivethirtyeight") X, y = make_circles(n_samples = 100, noise = 0.02) plt.scatter(X[:, 0], X[:, 1], s = 40, color ='g') plt.xlabel("X") plt.ylabel("Y") plt.show() plt.clf() |
Output:
make _circle with 100 data points
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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