Build a random forest model from scratch with the following conditions:

<ul>
<li>The model takes as input a dataframe <code>data</code> and an array <code>new_point</code> with length equal to the number of fields in the <code>data</code></li>
<li>All values of both <code>data</code> and <code>new_point</code> are <code>0</code> or <code>1</code>, i.e., all fields are dummy variables and there are only two classes</li>
<li>Rather than randomly deciding what subspace of the data each tree in the forest will use like usual, make your forest out of decision trees that go through every permutation of the value columns of the data frame and split the data according to the value seen in <code>new_point</code> for that column</li>
<li>Return the majority vote on the class of <code>new_point</code></li>
<li>You may use <code>pandas</code> and <code>NumPy</code> but NOT <code>scikit-learn</code></li>
</ul>

Bonus: The <code>permutations</code> in the <code>itertools</code> package can help you easily get all of any iterable object.

Example:

Input:

<pre tabindex="0" class="chroma"><code>new_point = [0,1,0,1]
print(data)
...
 Var1 Var2 Var3 Var4 Target
0 1.0 1.0 1.0 0.0 1
1 0.0 0.0 0.0 0.0 0
2 1.0 0.0 1.0 0.0 0
3 0.0 1.0 1.0 1.0 1
4 1.0 0.0 1.0 0.0 0
.. ... ... ... ... ...
95 0.0 1.0 0.0 1.0 0
96 1.0 1.0 0.0 0.0 0
97 0.0 0.0 1.0 1.0 0
98 1.0 0.0 0.0 0.0 0
99 0.0 1.0 0.0 0.0 0

[100 rows x 5 columns]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def random_forest(new_point, data) -&gt; 0
</code></pre>

Build a random forest model from scratch.

Random Forest from Scratch

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