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machine_learning/decision_tree.py
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@ -155,18 +155,18 @@ class TestDecisionTree:
def main(): def main():
""" """
In this demonstration first we are generating x which is a numpy array In this demonstration first we are generating x which is a numpy array
containing values starting from -1 to 1 with an interval of 0.005 containing values starting from -1 to 1 with an interval of 0.005
i.e [-1,-0.995,....,0.995,1] this is what we are getting by applying arange i.e [-1,-0.995,....,0.995,1] this is what we are getting by applying arange
function of numpy.Then the we are generating y by applying sin function function of numpy.Then the we are generating y by applying sin function
on x which is an array containing values from -1 to 1 with difference on x which is an array containing values from -1 to 1 with difference
of 0.005 i.e we are getting an array y which contains sin of each value of 0.005 i.e we are getting an array y which contains sin of each value
of x. We then train a decision tree on the data set and use the decision tree of x. We then train a decision tree on the data set and use the decision tree
to predict the label of 10 different test values. Here we should prefer to predict the label of 10 different test values. Here we should prefer
calculating Root Mean Squared Error over Mean Sqaured error because RMSE calculating Root Mean Squared Error over Mean Sqaured error because RMSE
should be used when you need to communicate your results in an understandable should be used when you need to communicate your results in an understandable
way to end users or when penalising outliers is less of a priority.Interpretation way to end users or when penalising outliers is less of a priority.Interpretation
will be easy in this case. You can check out https://stephenallwright.com/rmse-vs-mse/ to will be easy in this case. You can check out https://stephenallwright.com/rmse-vs-mse/ to
know more. know more.
""" """
x = np.arange(-1.0, 1.0, 0.005) x = np.arange(-1.0, 1.0, 0.005)