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## What Is Linear Discriminant Analysis?

Linear Discriminant analysis has become the most widely used dimensionality reduction technique. This technique is used in machine learning as there are many different data sets that exist in these days. This Discriminant analysis is created by Ronald A Fisher in 1936. However, the first developed Discriminant analysis has been used to solve only class-2 issues whereas the latest developed Discriminant analysis would be used to solve multi-class problems.

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This Discriminant analysis is used in machine learning for pre-processing the information and classifies the projects based on the patterns. When this analysis is used in Python, it helps to shrink the high dimensional data on the lower dimensional space. This analysis is mainly performed to reduce the dimensionality while saving the data based on the class discrimination. The linear Discriminant analysis is used to find out the features to linear combination. These features are used to characterize two or multiple objects or events. The combination that is obtained as a result would be used as linear classifier that in turn can be used to reduce the dimensionality.

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**How You Can Prepare Data For Performing Linear Discriminant Analysis?**

The following is the data that is prepared to use for Linear Discriminant analysis:

**Classification problems**: LDA is used for the purpose of solving classification issues where the output can be categorized. LDA would give support to both the binary classification and the multi-class classification.**Gaussian distribution**: When a standard model is implemented, it uses Gaussian distribution for inputting the variables. You can consider the univariate distribution that is for each attribute and make use of the transforms that are Gaussian.**Remove outliers**: You need to exclude the outliers from the data. This helps to skew the statistics that are used as a different class in LDA such as standard deviation and mean.**Same variance**: Every variable that is inputted would have some kind of variance that is the same. It is better to standardize the data prior to making use of LDA so that the mean would have ‘0’ value and the standard deviation would have 1 as the value.

**Extensions used in Linear Discriminant Analysis**

As linear Discriminant analysis is very easy to use and simple so it is widely used in different variations and extensions. These are designed to improve the efficacy of Linear Discriminant analysis. Few of the examples where this LDA is used include:

**Flexible Discriminant analysis (FDA):**The regularly used Linear Discriminant analysis would make use of inputs that are combination of linear variables. The flexible Discriminant analysis would let you to use the non-linear combination of inputs such as splines. If you are stuck in solving the assignment on this topic, you can seek the help of our experts. They are available round the clock to offer you the best assignment help.**Quadratic Discriminant analysis (QDA):**Every class in the quadratic Discriminant analysis would make have its own estimate for the variance when only a single input variable is there. When there are multiple input variables, every class would make use of its respective estimate of variance. Many students feel stressed and burdened to solve the assignment on this topic. Under this tight deadline, they complete the assignment which is inaccurate. As a result of which they lose the valuable grades. However, if you do not want to lose the grades, you can approach our experts for help. They are available all the time to offer you with the required help on linear Discriminant analysis topics. They are well versed with all the topics related to LDA.**Regularized Discriminant analysis (RDA):**This method will moderate the impact of different variables that are used in Linear Discriminant analysis. This is done by regularizing the estimate of variance. If you lack time to solve the topic due to other academic tasks or personal commitments, you can immediately hire our experts. They will solve the assignment before the given deadline. This helps you to submit the flawless assignment solutions to your professor and score good grades in the examination.

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