Distance is calculated between points and Norm calculated between vectors
What is Norm?
In simple words, the norm is a quantity that describes the size of a vector, something that we can represent using a set of numbers as you might already know that in machine learning everything is represented in terms of vectors. In this norm, all the components of the vector are weighted equally.
let’s take vector ‘a’ that contains two elements X1 and X2. We can represent the features of vector ‘a’ in two-dimensional (2d).
a = (2,5) and O(0,0)
Here I am highlighting my work on Allstate Claims Prediction, a Kaggle problem.
The Allstate Corporation is an American insurance company, headquartered in Northfield Township , Illinois, near Northbrook since 1967. Founded in 1931 as part of Sears, Roebuck and Co., it was spun off in 1993. The company also has personal lines insurance operations in Canada.
Allstate’s slogan “You’re in good hands” was created in the 1950s by Allstate Insurance Company’s sales executive, Davis W. Ellis. When you’ve been devastated by a serious car accident, your focus is on the things that matter the most: family, friends…
Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language. It is used for practical purposes that help us with everyday activities, such as texting, e-mail, and communicating across languages.
Everything we express (either verbally or in written) carries huge amounts of information. The topic we choose, our tone, our selection of words, everything adds some type of information that can be interpreted and value extracted from it. …
While Python’s Scikit-learn library provides the easy-to-use and efficient SGDClassifier , the objective of this post is to create an own implementation using without using sklearn. Implementing basic models is a great idea to improve your comprehension about how they work.
Create a custom dataset using make_classification inbuilt function from sklearn.
Above code generates dataset with shape of X with (50000, 15) and y (50000,))
Input values (x) are combined linearly using weights or coefficient values to predict an output value (y). A key difference from linear regression is that the output value being modeled is a binary values…
A Machine learning Enthusiast!