"It is a strategy of turning the higher dimensions dataset into fewer dimensions dataset while guaranteeing that it gives similar information," says one definition. These methods are commonly used in machine learning to develop a more accurate predictive model when tackling classification problems.
For instance, we may combine Dum Dums and Blow Pops to examine all lollipops at once. In both of these cases, dimensionality reduction can aid. Dimensionality reduction can be accomplished in two ways: Selection of features: From the initial feature set, we select a subset of features.
It cuts down on the amount of time and storage space needed. The reduction of multicollinearity improves the interpretation of machine learning model parameters. When data is reduced to very low dimensions, such as 2D or 3D, it becomes easier to visualise. Reduce the number of variables in your space.
Depending on the method, dimensionality reduction might be linear or non-linear. The principal linear approach, often known as Principal Component Analysis, or PCA, is explored further down.
It's one of the most widely used programmes for exploratory data analysis and predictive modelling. The variance of each characteristic is taken into account by PCA since the high attribute indicates a good separation between the classes and so minimises dimensionality.
Learner's Ratings
4.3
Overall Rating
67%
11%
12%
5%
5%
Reviews
S
Suresh Kumar
5
Hi Sir,
I want a clearity up on these
1. To learn Data Science "Machine learning" is part of it but we have to learn additionally python libraries (panda, numpy, matplotlib) or else in ML enough.
A
Ayush Bharti
4
how can i download the finaldata.csv?
J
Jagannath Mahato
5
Hello Kushal Sir!
Your way of teaching is very good. I thank you from my heart ❤️ that you are providing such good content for free.
M
Muhammad Qasim
5
Hi Kushal ! Your way of teaching is extremely helpful and you are one of the best teacher in the world.
Extremely helpful and I recommend to my peer as well for this course.
S
Shafi Akhtar
5
None
A
Aniket Kumar prasad
5
Very helpful and easy to understand all the concepts, best teacher for learning ML.
R
Rishu Shrivastav
5
explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?
V
VIKAS CHOUBEY
5
very nicely explained
V
Vrushali Kandesar
5
Awesome and very nicely explained!!!
One importing thing to notify to team is by mistakenly navie's practical has been added under svm lecture and vice versa (Learning Practical 1)
Share a personalized message with your friends.