What do you observe about the model performances

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Reference no: EM133430048

Questions: The objective of this lab is Build a SVM model for Face Recognition Problem
We will use a very famous dataset, called Labelled Faces in the Wild, which consists of 1288 faces of famous people, and it is available at Canvas, and also,
You can find more information about the data used in this lab here:
Each image consists of 1850 features: we could proceed by simply using each of them in the model.
Fitting a SVM to non-linear data using the Kernel Trick produces non- linear decision boundaries. In particular, we seek to build SVM model with a radial basis function (RBF) kernel.
Use a grid search cross-validation to explore random combinations of parameters.
Steps to do:
1. Loading the data
2. Print the field names (that is, the keys to the dictionary)
3. Print the dataset description contained. Print the data, its shape, and the target names.
4. Divide the data into features (X) using the faces. data and target (y) using faces. target.
5. Splitting the data into training and testing sets. We train the model with 70% of the samples and test with the remaining 30%.
6. Declare SVM model with kernel='rbf', class_weight='balanced'
7. Use a grid search cross-validationwith 10 CV to explore random combinations of parameters.
8. predict on the test set, using the best model from above step.
9. Model performances
10. What do you observe about the model performances?

Reference no: EM133430048

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