nearest neighbor
by comparing the similarity between train and test data to take the most similar data’s label as test data predicted label
- similarity comparasion
- L1 distance
- L2 distance
implementation
class NearestNeighbor(object):
def __init__(self):
pass
def train(self, X, Y):
self.Xtr = X
self.Ytr = Y
def predict_L1(self, X):
num_test = X.shape[0]
Ypred = np.zeros(num_test)
for i in range(num_test):
#print("i: " ,i)
distances = np.sum(np.abs(self.Xtr - X[i, :]), axis=1)
min_index = np.argmin(distances)
Ypred[i] = self.Ytr[min_index]
return Ypred
def predict_L2(self, X):
num_test = X.shape[0]
Ypred = np.zeros(num_test)
for i in range(num_test):
#print("i: " ,i)
distances = np.sqrt(np.sum(np.square(self.Xtr - X[i,:]), axis = 1))
min_index = np.argmin(distances)
Ypred[i] = self.Ytr[min_index]
return Ypred
nn = NearestNeighbor()
nn.train(Xtr, Ytr)
Yte_predict = nn.predict_L2(Xte)
K nearest neighbors
instead of only 1 most similar data K most similar datas are selected by comparasion and using the most appeared label as predicted label. It is one of most famous machine learning model for classification problem.
#Common imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
#Import the data set
raw_data = pd.read_csv('classified_data.csv', index_col = 0)
#Import standardization functions from scikit-learn
from sklearn.preprocessing import StandardScaler
#Standardize the data set
scaler = StandardScaler()
scaler.fit(raw_data.drop('TARGET CLASS', axis=1))
scaled_features = scaler.transform(raw_data.drop('TARGET CLASS', axis=1))
scaled_data = pd.DataFrame(scaled_features, columns = raw_data.drop('TARGET CLASS', axis=1).columns)
#Split the data set into training data and test data
from sklearn.model_selection import train_test_split
x = scaled_data
y = raw_data['TARGET CLASS']
x_training_data, x_test_data, y_training_data, y_test_data = train_test_split(x, y, test_size = 0.3)
#Train the model and make predictions
from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(n_neighbors = 1)
model.fit(x_training_data, y_training_data)
predictions = model.predict(x_test_data)
#Performance measurement
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
print(classification_report(y_test_data, predictions))
print(confusion_matrix(y_test_data, predictions))
#Selecting an optimal K value Using the Elbow Method
error_rates = []
for i in np.arange(1, 101):
new_model = KNeighborsClassifier(n_neighbors = i)
new_model.fit(x_training_data, y_training_data)
new_predictions = new_model.predict(x_test_data)
error_rates.append(np.mean(new_predictions != y_test_data))
plt.figure(figsize=(16,12))
plt.plot(error_rates)
