mathdatech/Python
1
0
Fork 0
mirror of https://github.com/TheAlgorithms/Python.git synced 2025-02-25 18:38:39 +00:00
Code Issues Packages Projects Releases Wiki Activity

[pre-commit.ci] auto fixes from pre-commit.com hooks

Browse Source 
for more information, see https://pre-commit.ci
This commit is contained in:
pre-commit-ci[bot] 2023-08-17 07:57:04 +00:00
parent 6aa0287ba2
commit a38e2ef826
1 changed files with 9 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

18
machine_learning/decision_tree.py
View File

@ -155,18 +155,18 @@ class TestDecisionTree:
def main():
"""
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
i.e [-1,-0.995,....,0.995,1] this is what we are getting by applying arange
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
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
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
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 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
calculating Root Mean Squared Error over Mean Sqaured error because RMSE
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
will be easy in this case. You can check out https://stephenallwright.com/rmse-vs-mse/ to
calculating Root Mean Squared Error over Mean Sqaured error because RMSE
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
will be easy in this case. You can check out https://stephenallwright.com/rmse-vs-mse/ to
know more.
"""
x = np.arange(-1.0, 1.0, 0.005)