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Further, there are four variables in addition to the species classification: petal length, petal width, sepal length and sepal width. For the purposes of illustration, we’ll train our perceptron using only petal length and width and drop the other two measurements. These data transformations result in the following plot of the remaining two species in the two-dimensional feature space of petal length and petal width: The plot suggests that petal length and petal width are strong predictors of species – at least in our training data set. Can a perceptron learn to tell them apart. Training our perceptron is simply a matter of initializing the weights (here we initialize them to zero) and then implementing the perceptron learning rule, which just updates the weights based on the error of each observation with the current weights. 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