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    <h3>Your first segment of code defines a classifier on <code>1d</code> data.</h3> <p><code>X</code> represents the feature vectors.</p> <pre><code>[0] is the feature vector of the first data example [1] is the feature vector of the second data example .... [[0],[1],[2],[3]] is a list of all data examples, each example has only 1 feature. </code></pre> <p><code>y</code> represents the labels.</p> <p>Below graph shows the idea:</p> <p><img src="https://i.stack.imgur.com/umFVj.png" alt="enter image description here"></p> <ul> <li>Green nodes are data with label 0</li> <li>Red nodes are data with label 1 </li> <li>Grey nodes are data with unknown labels. </li> </ul> <pre> print(neigh.predict([[1.1]])) </pre> <p>This is asking the classifier to predict a label for <code>x=1.1</code>. </p> <pre><code> print(neigh.predict_proba([[0.9]])) </code></pre> <p>This is asking the classifier to give membership probability estimate for each label.</p> <p>Since both grey nodes located closer to the green, below outputs make sense.</p> <pre><code> [0] # green label [[ 0.66666667 0.33333333]] # green label has greater probability </code></pre> <h3>The second segment of code actually has good instructions on <code>scikit-learn</code>:</h3> <blockquote> <p>In the following example, we construct a NeighborsClassifier class from an array representing our data set and ask who’s the closest point to [1,1,1]</p> </blockquote> <pre> >>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(n_neighbors=1) >>> neigh.fit(samples) NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> print(neigh.kneighbors([1., 1., 1.])) (array([[ 0.5]]), array([[2]]...)) </pre> <p>There is no target value here because this is only a <code>NearestNeighbors</code> class, it's not a classifier, hence no labels are needed. </p> <h3>For your own problem:</h3> <p>Since you need a classifier, you should resort to <code>KNeighborsClassifier</code> if you want to use <code>KNN</code> approach. You might want to construct your feature vector <code>X</code> and label <code>y</code> as below:</p> <pre><code>X = [ [h1, e1, s1], [h2, e2, s2], ... ] y = [label1, label2, ..., ] </code></pre>
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    1. POImplement K Neighbors Classifier in scikit-learn with 3 feature per object
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    1. POImplement K Neighbors Classifier in scikit-learn with 3 feature per object
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    1. COLast question: How can i choose labels? Why it is [0,0,1,1] and not [0,1,2,3] or [0,0,0,1] ?
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    2. COI got errors: "setting an array element with a sequence" because fit function wants: X : {array-like, sparse matrix, BallTree, cKDTree}. I got a list of list! If i insert in feature vectors all the 7 humoments: "query data dimension must match BallTree data dimension" i tried also to covert it in numpy array, same error : "setting an array element with a sequence."
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    3. COThe label is [0,0,1,1] not [0,1,2,3] because in that example we only have 2 classes not 4 classes. It could be [0,0,0,1] if the three left-most nodes are actually the same class. Can you post your code in a different post for your second question?
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