One of the widely used dataset for image classification is the MNIST dataset [LeCun et al., 1998].While it had a good run as a benchmark dataset, even simple models by today’s standards achieve classification accuracy over 95%, making it unsuitable for … The variable names are as follows: The baseline performance of predicting the most prevalent class is a classification accuracy of approximately 26%. print(description), output:- KNN can be useful in case of nonlinear data. Classification Predictive Modeling 2. Top results achieve a classification accuracy of approximately 77%. The final column, our classification target, is the particular species—one of three—of that iris: setosa, versicolor, or virginica. 50% 3.000000 117.000000 72.000000 23.000000 30.500000 32.000000 This is pre-trained on the ImageNet dataset, a large dataset consisting of 1.4M images and 1000 classes. It is a regression problem. dog … rat. TAX: full-value property-tax rate per $10,000. 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