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scikit-learn: Decision Trees

Unsolved
Supervised

Difficulty: 2 | Problem written by zeyad_omar

Educational Resource: https://towardsdatascience.com/a-beginners-guide-to-scikit-learn-14b7e51d71a4


Problem reported in interviews at

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A decision tree classifier is a supervised learning technique that is used to classifiy the data based on some pivot points.

For example, if you can not decide whether to go outside or not so you ask your self "Is it rainy outside?", "Do i like playing football?", and "Will I enjoy my time with those I am going to play with?"

Based on the answers to the questions at each node of the decision tree, the classifier makes a decision (classification).

In this probelm you are required to use sklearn to implement the decision tree classifier.

Sample Input:
<class 'list'>
X_train: [[0, 0], [1, 1]]
<class 'list'>
y_train: [0, 1]
<class 'list'>
X_test: [[2, 2]]

Expected Output:
<class 'numpy.ndarray'>
[1]

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numpy has been already imported as np (import numpy as np)