KNN classifier is a very popular and simple machine learning model. It’s a memory-based supervised machine learning model.
What is a supervised learning model?
I will explain it in detail. But here is what Wikipedia has to say:
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.
Supervised learning models take input features (X) and output (y) to train a model. The goal of the model is to define a function that can use the input features and calculate the output.
An example will make it more clear
Here is a dataset that contains the mass, width, height, and color_score of some fruit samples.
The purpose of this dataset was to train a model so that if we input the mass, width, height, and color-score to the model, the model can let us know the name of the fruit. Like if we input the mass, width, height, and color_score of a piece of fruit as 175, 7.3, 7.2, 0.61 respectively, the model should output the name of the fruit as an apple.
Here mass, width, height, and color_score are the input features(X). And the name of the fruit is the output variable or label(y).
This example may sound silly to you. But this is the mechanism that is used in very high level supervised machine learning models.
I will show a practical example with a real dataset later.
KNN Classifier
As I mentioned in the beginning, the KNN classifier is an example of a memory-based machine learning model.
That means this model memorizes the labeled training examples and they use that to classify the objects it hasn’t seen before.
The k in KNN classifier is the number of training examples it will retrieve in order to predict a new test example.
KNN classifier works in three steps:
- When it is given a new instance or example to classify, it will retrieve training examples that it memorized before and find the k number of closest examples from it.
- Then the classifier looks up the labels (the name of the fruit in the example above) of those k numbers of closest examples.
- Finally, the model combines those labels to make a prediction. Usually, it will predict the majority labels. For example, if we choose our k to be 5, from the closest 5 examples, if we have 3 oranges and 2 apples, the prediction for the new instance will be orange.
Data Preparation
Before we start, I encourage you to check if you have the following resources available in your computer:
If you do not have Jupyter Notebook installed, use any other notebook of your choice. I suggest a Google Colaboratory notebook. Follow this link to start. Just remember one thing,
Google Colaboratory notebook is not private. So, do not do any professional or sensitive work there. But great for practice. Because lots of commonly used packages are already installed in it.
I suggest, download the dataset. I provided the link at the bottom of the page. Run every line of code yourself if you are reading to learn this.
First, import the necessary libraries:
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_splitFor this tutorial, I will use the Titanic dataset from Kaggle. I have this dataset uploaded in the same folder as my notebook.
Here is how I can import the dataset in the notebook using pandas.
titanic = pd.read_csv('titanic_data.csv')
titanic.head()#titaninc.head() gives the first five rows of the dataset. We will #print first five rows only to examine the dataset.
Look at the second column. It contains the information, if the person is survived or not. 0 means the person survived and 1 means the person did not survive.
For this tutorial, our goal will be to predict the ‘Survived’ feature.
To make it simple, I will keep a few key features that are more important for the algorithm and get rid of the rest.
This dataset is very simple. Just from intuition, we can see that there are columns that cannot be important to predict the ‘Survived’ feature.
For example, ‘PassengerId’, ‘Name’, ‘Ticket’ and, ‘Cabin’ does not seem to be useful to predict that if a passenger is survived or not.
I will make a new DataFrame with a few key features and name the new DataFrame titanic1.
titanic1 = titanic[['Pclass', 'Sex', 'Fare', 'Survived']]The ‘Sex’ column has the string value and that needs to be changed. Because computers do not understand words. It only understands numbers. I will change the ‘male’ for 0 and ‘female’ for 1.
titanic1['Sex'] = titanic1.Sex.replace({'male':0, 'female':1})This is how the DataFrame titanic1 looks like:
Our goal is to predict the ‘Survived’ parameter, based on the other information in the titanic1 DataFram. So, the output variable or label(y) is ‘Survived’. The input features(X) are ‘P-class’, ‘Sex’, and, ‘Fare’.
X = titanic1[['Pclass', 'Sex', 'Fare']]
y = titanic1['Survived']Develop a KNN Classifier
To start with, we need to split the dataset into two sets: a training set and a test set.
We will use the training set to train the model where the model will memorize both the input features and the output variable.
Then we will use the test set to see that if the model can predict if the passenger survived using the ‘P-class’, ‘Sex’, and, ‘Fare’.
The method ‘train_test_split’ is going to help to split the data. By default, this function uses 75% data for the training set and 25% data for the test set. If you want you can change that and you can specify the ‘train_size’ and ‘test_size’.
If you put train_size 0.8, the split will be 80% training data and 20% test data. But for me the default value 75% is good. So, I am not using train_siz or test_size parameters.
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)Remember to use the same value for ‘random_state’. That way, every time you will do this split, it will take the same data for the training set and test set.
I chose random_state as 0. You can choose a number of your choice.
Python’s scikit -learn library, already have a KNN classifier model. I will import that.
from sklearn.neighbors import KNeighborsClassifierSave this classifier in a variable.
knn = KNeighborsClassifier(n_neighbors = 5)Here, n_neighbors is 5.
That means when we will ask our trained model to predict the survival chance of a new instance, it will take 5 closest training data.
Based on the labels of those 5 training data, the model will predict the label of the new instance.
Now, I will fit the training data to the model so that model can memorize them.
knn.fit(X_train, y_train)You may think that as it memorized that training data it can predict the label of 100% of the training features correctly. But that’s not certain. Why?
Look, whenever we give input and ask it to predict the label it will take a vote from the 5 closest neighbors even if it has the exact same feature memorized.
Let’s see how much accuracy it can give us on training data
knn.score(X_train, y_train)The training data accuracy I got is 0.83 or 83%.
Remember, we have a test dataset that our model has never seen. Now check, how much accurately it can predict the label of the test dataset.
knn.score(X_test, y_test)The accuracy came out to be 0.78 or 78%.
Combining the codes above, here is the 4 lines of code that makes your classifier:
knn = KNeighborsClassifier(n_neighbors = 5)
knn.fit(X_train, y_train)
knn.score(X_train, y_train)
knn.score(X_test, y_test)Congrats! You developed a KNN classifier!
Notice, the training set accuracy is a bit higher than the test set accuracy. That’s overfitting.
What is Overfitting?
Sometimes the model learns the training set so well that it can predict the training dataset labels very well. But when we ask the model to predict with a test dataset or a dataset that it did not see before, it does not perform as well as the training dataset. This phenomenon is called overfitting.
In a single sentence, when the training set accuracy is higher than the test set accuracy, we call it overfitting.
Prediction
If you want to see the predicted output for the test dataset, here is how to do that:
Input:
y_pred = knn.predict(X_test)y_pred
Output:
array([0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0,
0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0,
1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1,
1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,
0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,
0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0,
1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1,
1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,
0, 1, 1], dtype=int64)Or you can just input one single example and find the label.
I want to see when a person is traveling in ‘P-class’ 3, ‘Sex’ is female that means 1, and, paid a ‘Fare’ of 25, if she could survive as per our model.
Input:
knn.predict([[3, 1, 25]])Remember to use two brackets, because it requires a 2D array
Output:
array([0], dtype=int64)The output is zero. That means as per our trained model the person could not survive.
Please feel free try wth more different inputs like this one!
If You Want to See Some Further Analysis of KNN Classifier
KNN classifier is highly sensitive to the choice of ‘k’ or n_neighbors. In the example above I used n_neighors 5.
For different n_neighbors, the classifier will perform differently.
Let’s check how it performs on the training dataset and test dataset for different n_neighbors value. I choose 1 to 20.
Now, we will calculate the training set accuracy and the test set accuracy for each n_neighbors value from 1 to 20,
training_accuracy = []
test_accuracy = []
for i in range(1, 21):
knn = KNeighborsClassifier(n_neighbors = i)
knn.fit(X_train, y_train)
training_accuracy.append(knn.score(X_train, y_train))
test_accuracy.append(knn.score(X_test, y_test))After running this code snippet, I got the training and test accuracy for different n_neighbors.
Now, let’s plot the training and test set accuracy against n_neighbors in the same plot.
plt.figure()
plt.plot(range(1, 21), training_accuracy, label='Training Accuarcy')
plt.plot(range(1, 21), test_accuracy, label='Testing Accuarcy')
plt.title('Training Accuracy vs Test Accuracy')
plt.xlabel('n_neighbors')
plt.ylabel('Accuracy')
plt.ylim([0.7, 0.9])
plt.legend(loc='best')
plt.show()
Analyze the Graph Above
In the beginning, when the n_neighbors were 1, 2, or 3, training accuracy was a lot higher than test accuracy. So, the model was suffering from high overfitting.
After that training and test accuracy became closer. That is the sweet spot. We want that.
But when n_neighbors was going even higher, both training and test set accuracy was going down. We do not need that.
From the graph above, the perfect n_neighbors for this particular dataset and model should be 6 or 7.
That is a good classifier!
Look at the graph above! When n_neighbors is about 7, both training and testing accuracy was above 80%.
Here is link to the complete code
Conclusion
I hope you learned to build a nice KNN classifier and will try it on different datasets. Please do not hesitate to ask, if you have any questions and share if you do any new project with this.
Thank you so much for reading this article! Here is the titanic dataset I used for this tutorial
#machinelearning #datascience #datascientists #python #scikitlearn #knnclassifier
