In the three-class classification problem defined in 4-dimensional space, the classes , The feature vectors extracted from the samples are given below.

The classes , show Gaussian distributions (, ), (, ) and (, ) respectively, and their class presumptions are equal.

In the feature selection stage, Principal Component Analysis (PCA) method is applied to the given features. will be applied and the vector size will be reduced to 3.

the class training and test samples using 3-fold cross validation technique will be determined

Support Vector Machines (SVM), Linear Discriminant Analysis at the classification stage (FLDA), 3-Nearest Neighbor (k-NN 3-NN) and Bayesian classifiers will be used.

Sensitivity (SNS), precision (SPC) and accuracy (ACC) metrics for performance evaluation will be used.

In line with this information, fill in the table below.

(NOTE:PLEASE HELP ME I HAVE 1.30 HOURS)

1.	Average of class after feature selection stage
2.	Number of experiments to be performed (per classifier)
3.	Number of examples to be used in the training phase (per class)
4.	Number of samples to be used in the testing phase (per class)
5.	Percent precision of class obtained by Support Vector Machines classifier
6.	Percent precision of class obtained by Support Vector Machines classifier
7.	Percent accuracy of class obtained with Support Vector Machines classifier
8.	Percent precision of the class obtained with the Linear Discriminant Analysis classifier
9.	Percent precision of the class obtained by Linear Discriminant Analysis
10.	Percent accuracy of class obtained with Linear Discriminant Analysis classifier
11.	3-The overall percentage precision obtained with the Nearest Neighbor classifier rate
12.	3-The overall percentage accuracy obtained with the Nearest Neighbor classifier
13.	3-Overall percent accuracy obtained with the Nearest Neighbor classifier
14.	If feature selection was not performed (using All Space feature vectors), it would be classified as obtained by 3-Nearest Neighbor classifier. percent accuracy
15.	If feature selection was not performed (using All Space feature vectors), it would be classified as obtained by 3-Nearest Neighbor classifier. percent certainty =?
16.	If feature selection was not performed (using All Space feature vectors), it would be classified as obtained by 3-Nearest Neighbor classifier. percent accuracy =?

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