LDA creates predictions by calculating the likelihood that a fresh set of inputs falls into each of the classes. The output class is the one with the highest probability, and a forecast is produced.
The goal of LDA is to find a feature subspace that maximises group separability. While principal component analysis is an unsupervised Dimensionality reduction technique, it does not take into account the class label. PCA focuses on capturing the data set's highest variation direction.
The likelihood that a fresh set of inputs belongs to each class is estimated using linear discriminant analysis. The output class with the highest probability is chosen. Here's how Bayes' theorem estimates the chance that the data belongs to each class if the output class is (k) and the input is (x).
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Rishu Shrivastav
5
explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?
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VIKAS CHOUBEY
5
very nicely explained
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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)
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Mohd Mushraf
5
Amazing Teaching
J
Juboraj Juboraj
5
Easy to understand & explain details.
J
Joydeb
5
Awesome Course sir and your teaching style is very GOOD.
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Shaga Chandrakanth Goud
5
Hi Kushal ji, Thanks a lot for a very good explanation. I have doubts about where we can get the dataset that you explained in the video. Can you make it available in resource ,so that we can downld
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Neel Khairnar
5
Kushal is very good explainer he is covering all topics nicely 👍
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