SVM works by finding a hyperplane in an N-dimensional space (N number of features) which fits to the multidimensional data while considering a margin.
The numpy.sum() is used to sum of array elements over a given axis. It takes a lot of parameters like dtype, out, initial, and keepdims.keepdims is a boolean type parameter, if this is set to True, the axes which are reduced are left in the result as dimensions with size one. ...
PyOD is an awesome outlier detection library. In this article learn what is outlier and how to use PyOD library for outlier detection in Python.
In the following example, I am using the IRIS dataset. I have taken the code reference from the repository. Note: tf.disable_v2_behaviour() is used to use the Tensorflow 1 functionalities, as i have Tensorflow 2 installed on my PC. import matplotlib.pyplot as plt import numpy as np ...
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) titles = ['SVC with linear kernel', 'LinearSVC (linear kernel)'] for i, clf in enumerate((svc, lin_svc)): plt.subplot(1, 2, i + 1) ...