Machine Learning for Beginners in Python: K-Nearest Neighbors Classification

K-Nearest Neighbors Classification


import pandas as pd
from sklearn import neighbors
import numpy as np
%matplotlib inline  
import seaborn

Create Dataset

Here we create three variables, test_1 and test_2 are our independent variables, ‘outcome’ is our dependent variable. We will use this data to train our learner.

training_data = pd.DataFrame()

training_data['test_1'] = [0.3051,0.4949,0.6974,0.3769,0.2231,0.341,0.4436,0.5897,0.6308,0.5]
training_data['test_2'] = [0.5846,0.2654,0.2615,0.4538,0.4615,0.8308,0.4962,0.3269,0.5346,0.6731]
training_data['outcome'] = ['win','win','win','win','win','loss','loss','loss','loss','loss']

test_1 test_2 outcome
0 0.3051 0.5846 win
1 0.4949 0.2654 win
2 0.6974 0.2615 win
3 0.3769 0.4538 win
4 0.2231 0.4615 win

Plot the data

This is not necessary, but because we only have three variables, we can plot the training dataset. The X and Y axes are the independent variables, while the colors of the points are their classes.

seaborn.lmplot('test_1', 'test_2', data=training_data, fit_reg=False,hue="outcome", scatter_kws={"marker": "D","s": 100})
<seaborn.axisgrid.FacetGrid at 0x11008aeb8>


Convert Data Into np.arrays

The scikit-learn library requires the data be formatted as a numpy array. Here are doing that reformatting.

X = training_data.as_matrix(columns=['test_1', 'test_2'])
y = np.array(training_data['outcome'])

Train The Learner

This is our big moment. We train a KNN learner using the parameters that an observation’s neighborhood is its three closest neighors. weights = 'uniform' can be thought of as the voting system used. For example, uniform means that all neighbors get an equally weighted “vote” about an observation’s class while weights = 'distance' would tell the learner to weigh each observation’s “vote” by its distance from the observation we are classifying.

clf = neighbors.KNeighborsClassifier(3, weights = 'uniform')
trained_model =, y)

View The Model’s Score

How good is our trained model compared to our training data?

trained_model.score(X, y)

Our model is 80% accurate!

Note: that in any real world example we’d want to compare the trained model to some holdout test data. But since this is a toy example I used the training data.

Apply The Learner To A New Data Point

Now that we have trained our model, we can predict the class any new observation, ytest. Let us do that now!

/* Create a new observation with the value of the first independent variable, 'test_1', as .4 
   and the second independent variable, test_1', as .6 */
x_test = np.array([[.4,.6]])

/* Apply the learner to the new, unclassified observation. */
array(['loss'], dtype=object)

Huzzah! We can see that the learner has predicted that the new observation’s class is loss.

We can even look at the probabilities the learner assigned to each class:

array([[ 0.66666667,  0.33333333]])

According to this result, the model predicted that the observation was loss with a ~67% probability and win with a ~33% probability. Because the observation had a greater probability of being loss, it predicted that class for the observation.


  • The choice of K has major affects on the classifer created.
  • The greater the K, more linear (high bias and low variance) the decision boundary.
  • There are a variety of ways to measure distance, two popular being simple euclidean distance and cosine similarity.


Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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