Python for Data Science Chapter 6 - Other Popular Machine Learning Models Models

Segment 3 - Instance-based learning w/ k-Nearest Neighbor

K-Nearest Neighbor Classification

A supervised classifier that memorizes observations from within a test set to predict classification labels for new, unlabeled observations

KNN makes predictions based on how similar training observations are to the new, incoming observations.

The more similar the observation values, the more likely they will be classified with the same label.

K-Nearest Neighbor Use Cases

Stock Price Prediction
Credit Risk Analysis
Predictive Trip Planning
Recommendation Systems

KNN Model Assumptions

Dataset has little noise
Dataset is labeled
Dataset only contains relevant features
Dataset has distinguishable subgroups
Avoid using KNN on large datasets It will probably take a long time

Setting up for classification analysis

import numpy as np
import pandas as pd
import scipy
import urllib
import sklearn

import matplotlib.pyplot as plt
from pylab import rcParams

from sklearn import neighbors
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn import metrics

from sklearn.neighbors import KNeighborsClassifier

np.set_printoptions(precision=4, suppress=True) 
%matplotlib inline
rcParams['figure.figsize'] = 7, 4
plt.style.use('seaborn-whitegrid')

Importing your data

address = '~/Data/mtcars.csv'

cars = pd.read_csv(address)
cars.columns = ['car_names','mpg','cyl','disp', 'hp', 'drat', 'wt', 'qsec', 'vs', 'am', 'gear', 'carb']

X_prime = cars[['mpg','disp','hp','wt']].values
y = cars.iloc[:,9].values

X = preprocessing.scale(X_prime)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=17)

Building and training your model with training data

clf = neighbors.KNeighborsClassifier()
clf.fit(X_train,y_train)
print(clf)

KNeighborsClassifier()

Evaluating your model's predictions

y_pred = clf.predict(X_test)
y_expect = y_test

print(metrics.classification_report(y_expect, y_pred))

              precision    recall  f1-score   support

           0       0.80      1.00      0.89         4
           1       1.00      0.67      0.80         3

    accuracy                           0.86         7
   macro avg       0.90      0.83      0.84         7
weighted avg       0.89      0.86      0.85         7

Recall: a measure of your model's completeness.

Of all your points that were labeled 1, only 67% of the results that were retuned were truly relevant
Of the entire dataset, 83% of the results that were returned were truly relevant

High precision + Low recall = Few results returned, but many of the label predictions that are returned are correct.