Understanding KNN(K-nearest neighbor) with example

KNN probably is one of the simplest but strong supervised learning algorithms used for classification as well regression purposes. Knn is most commonly used to classify the data points that are separated into several classes, in order to make prediction for new sample data points. It is a non-parametric and lazy learning algorithm. It classifies the data points based on the similarity measure (e.g. distance measures, mostly Euclidean distance).
 
Assumption of KNN: K- NN algorithm is based on the principle that, “the similar things exist closer to each other or Like things are near to each other.”
 
In this algorithm ‘K’ refers to the number of neighbors to consider for classification. It should be odd value.  The value of ‘K’ must be selected carefully otherwise it may cause defects in our model. If the value of ‘K’ is small then it causes Low Bias, High variance i.e. over fitting of model. In the same way if ‘K’ is very large then it leads to High Bias, Low variance i.e. under fitting of model. There are many researches done on selection of right value of K, however in most of the cases taking ‘K’ = {square-root of (total number of data ‘n’)} gives pretty good result. If the value ‘K’ comes to be odd then it’s all right else we make it odd either by adding or subtracting 1 from it.
 
K-NN
Classification for k=3

K-NN works pretty well with a small number of input variables (p), but there are more chances of error in prediction when the number of inputs becomes very large.

 
It is based on the simple mathematics that we used in high school level to measure the distance between two data points in graph. Some of the distance measuring techniques (Formulae) that we can use for K-NN classification are:
 

  •         Euclidean Distance:
Euclidean Distance
fig: Euclidean Distance

 

  •         Manhattan Distance:

 

Manhattan Distance
fig: Manhattan Distance
 
  •         Minkowski Distance:

 

Minkowski distance
fig: Minkowski Distance

For p=1, we get Manhattan Distance and for p=2, we get Euclidean Distance. So, we can say that Minkowski distance is generalized form of Manhattan Distance, Euclidean Distance.
 
Among these methods, Euclidean Distance method is widely used.
 

Algorithm for K-NN:

1.  Load the given data file into your program
2.  Initialize the number of neighbor to be considered i.e. ‘K’ (must be odd).
3.  Now for each tuples (entries or data point) in the data file we perform:
                      i.  Calculate distance between the data point (tuple) to be classified and each data points in the given data file.
                    ii.  Then add the distances corresponding to data points (data entries) in given data file (probably by adding column for distance).
                  iii.  Sort the data in data file from smallest to largest (in ascending order) by the distances.
4.  Pick the first K entries from the sorted collection of data.
5.  Observe the labels of the selected K entries.
6.  For classification, return the mode of the K labels and for regression, return the mean of K labels.

Decision Boundary for K-NN:

Decision boundary for classification using K-NN algorithm
source: stackoverflow.com

Example Of KNN(using Scikit learn)

We are going to classify the iris data into its different species by observing different 4 features: sepal length, sepal width, petal length, petal width. We have all together 150 observations(tuples) and we will make KNN classifying model on the basis of these observations.Link to download iris dataset- iris.csv

If we try to implement KNN from scratch it becomes a bit tricky however, there are some libraries like sklearn in python, that allows a programmer to make KNN model easily without using deep idea of mathematics. 
     
Let’s learn step-by-step how to implement KNN using scikit learn(sklearn).  

Step-1: First of all we load/import our training data set either from computer hard disk or from any url.
import pandas as pd# loading data file into the program. give the location of your csv file
dataset = pd.read_csv(“E:/input/iris.csv”)
print(dataset.head()) # prints first five tuples of your data.


Step-2: Now, we split data row wise into attribute/features and their corresponding labels.

 

X = dataset.iloc[:, :-1].values # splits the data and make separate array X to hold attributes.
y = dataset.iloc[:, 4].values  # splits the data and make separate array y to hold corresponding labels.


Step-3: In this step, we divide our entire dataset into two subset. one of them is used for training our model and the remaining one for testing the model. we divide our data into 80:20 i.e. first 80% of total data is training data and remaining 20% is our test data. We divide both attributes and labels. We do this type of division to measure the accuracy of our model.  This process of spiting our supplied dataset into training and testing subsets in order to know the accuracy and performance of our model is called cross-validation.

 

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)


Step-4: In this step, we perform normalization/standardization. It is process of re-scaling our data, so that the variations present in our data will not affect the accuracy of model. we have used z-score normalization technique here. For more on normalization, click here.

 

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)


Step-5: Now its time to define our KNN model.We make a model,and supply attributes of test subset for the prediction.

 

from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors=9) #defining KNN classifier for k=9.
classifier.fit(X_train, y_train) #learning process i.e. supplying training data to model
y_pred = classifier.predict(X_test) #stores prediction result in y_pred


Step-6: Since the test data we’ve supplied to the mdel is a portion of training data, so we have the actual labels for them. In this step we find the magnitudes of some classification metrices like precision, recall, f1-score etc. 


from sklearn.metrics import classification_report, confusion_matrixprint(confusion_matrix(y_test, y_pred))print(classification_report(y_test, y_pred))


Step-7: supply actual test data to the model. 


# testing model by suppplying ramdom data
x_random =  [[-1.56697667 , 1.22358774, -1.56980273, -1.33046652],
[-2.21742620 , 3.08669365, -1.29593102,-1.07025858]]
y_random=(classifier.predict(x_random))print(y_random)

 
Let’s see the output of above program.

   sepal.length  sepal.width  petal.length  petal.width variety
0           5.1          3.5           1.4          0.2  Setosa
1           4.9          3.0           1.4          0.2  Setosa
2           4.7          3.2           1.3          0.2  Setosa
3           4.6          3.1           1.5          0.2  Setosa
4           5.0          3.6           1.4          0.2  Setosa
[[11  0  0]
 [ 0  9  0]
 [ 0  0 10]]

Classification metrices for test data:
              precision    recall  f1-score   support

Setosa       1.00      1.00      1.00        11

  Versicolor       1.00      1.00      1.00         9
   Virginica       1.00      1.00      1.00        10

micro avg       1.00      1.00      1.00        30

   macro avg       1.00      1.00      1.00        30
weighted avg       1.00      1.00      1.00        30

For actual test data:


[‘Setosa’ ‘Setosa’]

 Advantages and Disadvantage of K-NN:

Advantages:
1.      The Kalgorithm is quiet easy and simple to implement as it does not include much of mathematics.
2.      We can do solve both classification and regression problem using K-NN algorithm.
Disadvantages:
1.      The algorithm becomes highly slower as the size of data increases.
 
 

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