The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning algorithms. It is one of the most widely used datasets for machine learning research.
The CIFAR-10 dataset consists of 60000 32x32 colored images in 10 classes, with 6000 images per class. There are 50,000 training images and 10,000 test images.
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We are Building a classifier for classifying 10,000 different images into ten unique classes that include the images of ten different animals such as dogs, horses, cats, and so on using the CIFAR-10 Dataset.
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For classification purposes, we have used a Supervised Learning Algorithm i.e., Random Forest
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For reducing the dimensionality, we are using principal component analysis (PCA)
- CIFAR-10 Dataset
- Supervised Learning
- Principal Component Analysis (PCA)
- Random Forest
- Classification Report