Predict Unknown
We provide training dataset points (features) and label (target). Next, we train the model using KNN classifier with k=3 (nearest neighbors). Finally, we are able now to predict the label for a new (unknown) data point.
from sklearn.neighbors import KNeighborsClassifier
from icecream import ic
# Training dataset
X = [[0,0],
[1,1],
[2,2],
[3,3]]
y = [0, 1, 0, 1]
# Train the model
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X, y)
# Make predictions for unknown
x_unknown = [1,2]
y_pred = knn.predict([x_unknown])
ic(x_unknown)
ic(y_pred);
"""
ic| x_unknown: [1, 2]
ic| y_pred: array([0])
"""
Data Frame / Fruits
We use pandas library to transform a json dataset into a DataFrame. Fruits dataset contains heights, widths and labels (fruit name).
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
from icecream import ic
# Training dataset
data = {
'height': [
3.91, 7.09, 10.48, 9.21, 7.95, 7.62, 7.95, 4.69, 7.50, 7.11,
4.15, 7.29, 8.49, 7.44, 7.86, 3.93, 4.40, 5.5, 8.10, 8.69
],
'width': [
5.76, 7.69, 7.32, 7.20, 5.90, 7.51, 5.32, 6.19, 5.99, 7.02,
5.60, 8.38, 6.52, 7.89, 7.60, 6.12, 5.90, 4.5, 6.15, 5.82
],
'fruit': [
'Mandarin', 'Apple', 'Lemon', 'Lemon', 'Lemon', 'Apple', 'Mandarin',
'Mandarin', 'Lemon', 'Apple', 'Mandarin', 'Apple', 'Lemon', 'Apple',
'Apple', 'Apple', 'Mandarin', 'Lemon', 'Lemon', 'Lemon'
]
}
# Transform dataset
df = pd.DataFrame(data)
df = df.sort_values(by=['fruit', 'width', 'height'])
X = df[['height', 'width']].values
y = df.fruit.values
# Train the model
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X, y)
# Make predictions
unknown_item = [9, 3]
unknown_items = [[9, 3], [4, 5], [2, 5], [8, 9], [5, 7]]
prediction = knn.predict([unknown_item])
predictions = knn.predict(unknown_items)
ic(df)
ic(unknown_item, prediction)
ic(unknown_items, predictions);
"""
ic| df: height width fruit
15 3.93 6.12 Apple
9 7.11 7.02 Apple
5 7.62 7.51 Apple
14 7.86 7.60 Apple
1 7.09 7.69 Apple
13 7.44 7.89 Apple
11 7.29 8.38 Apple
17 5.50 4.50 Lemon
19 8.69 5.82 Lemon
4 7.95 5.90 Lemon
8 7.50 5.99 Lemon
18 8.10 6.15 Lemon
12 8.49 6.52 Lemon
3 9.21 7.20 Lemon
2 10.48 7.32 Lemon
6 7.95 5.32 Mandarin
10 4.15 5.60 Mandarin
0 3.91 5.76 Mandarin
16 4.40 5.90 Mandarin
7 4.69 6.19 Mandarin
ic| unknown_item: [9, 3], prediction: array(['Lemon'])
ic| unknown_items: [[9, 3], [4, 5], [2, 5], [8, 9], [5, 7]]
predictions: array(['Lemon', 'Mandarin', 'Mandarin', 'Apple', 'Mandarin'])
"""
Model Evaluation
We split the dataset in two datasets, training and test and evaluate the model on both. The score is the difference between actual and predicted labels. A score of 1.0 means that the model correctly predicted all labels (100%).
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
# Training dataset
D1 = pd.DataFrame({
'height': [
3.91, 7.09, 10.48, 9.21, 7.95, 7.62, 7.95, 4.69, 7.50, 7.11,
4.15, 7.29, 8.49, 7.44, 7.86, 3.93, 4.40, 5.5, 8.10, 8.69
],
'width': [
5.76, 7.69, 7.32, 7.20, 5.90, 7.51, 5.32, 6.19, 5.99, 7.02,
5.60, 8.38, 6.52, 7.89, 7.60, 6.12, 5.90, 4.5, 6.15, 5.82
],
'fruit': [
'Mandarin', 'Apple', 'Lemon', 'Lemon', 'Lemon', 'Apple', 'Mandarin',
'Mandarin', 'Lemon', 'Apple', 'Mandarin', 'Apple', 'Lemon', 'Apple',
'Apple', 'Apple', 'Mandarin', 'Lemon', 'Lemon', 'Lemon'
]
})
# Test dataset
D2 = pd.DataFrame({
'height': [4, 4.47, 6.49, 7.51, 8.34],
'width': [6.5, 7.13, 7, 5.01, 4.23],
'fruit': ['Mandarin', 'Mandarin', 'Apple', 'Lemon', 'Lemon']
})
# Features and labels
X1 = D1[['height', 'width']].values
y1 = D1.fruit.values
X2 = D2[['height', 'width']].values
y2 = D2.fruit.values
# Train the model
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X1, y1)
# Evaluate the model
predictions1 = knn.predict(X1)
predictions2 = knn.predict(X2)
score1 = metrics.accuracy_score(y1, predictions1)
score2 = metrics.accuracy_score(y2, predictions2)
print("Model score on training dataset:", score1 * 100)
print("Model score on test dataset:", score2 * 100)
"""
Model score on training dataset: 85.0
Model score on test dataset: 100.0
"""
Score Graph
Models between k=3 and k=7 perform optimally on the test set. In those cases, optimal balance between overfitting and underfitting.
"""KNN classifier Score Graph / Fruit example
Models between k=3 and k=7 perform optimally on the test set.
In those cases, optimal balance between overfitting and underfitting.
"""
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
import matplotlib.pyplot as plt
# Training dataset
D1 = pd.DataFrame({
'height': [
3.91, 7.09, 10.48, 9.21, 7.95, 7.62, 7.95, 4.69, 7.50, 7.11,
4.15, 7.29, 8.49, 7.44, 7.86, 3.93, 4.40, 5.5, 8.10, 8.69
],
'width': [
5.76, 7.69, 7.32, 7.20, 5.90, 7.51, 5.32, 6.19, 5.99, 7.02,
5.60, 8.38, 6.52, 7.89, 7.60, 6.12, 5.90, 4.5, 6.15, 5.82
],
'fruit': [
'Mandarin', 'Apple', 'Lemon', 'Lemon', 'Lemon', 'Apple', 'Mandarin',
'Mandarin', 'Lemon', 'Apple', 'Mandarin', 'Apple', 'Lemon', 'Apple',
'Apple', 'Apple', 'Mandarin', 'Lemon', 'Lemon', 'Lemon'
]
})
# Test dataset
D2 = pd.DataFrame({
'height': [4, 4.47, 6.49, 7.51, 8.34],
'width': [6.5, 7.13, 7, 5.01, 4.23],
'fruit': ['Mandarin', 'Mandarin', 'Apple', 'Lemon', 'Lemon']
})
# Features and labels
X1 = D1[['height', 'width']].values
y1 = D1.fruit.values
X2 = D2[['height', 'width']].values
y2 = D2.fruit.values
# Initializa graph params
k = []
score1 = []
score2 = []
# Evaluate the score for different params
for i in range(len(X1)):
_k = i+1
clf = KNeighborsClassifier(n_neighbors = _k)
clf.fit(X1, y1)
_score1 = metrics.accuracy_score(y1, clf.predict(X1))
_score2 = metrics.accuracy_score(y2, clf.predict(X2))
k.append(_k)
score1.append(_score1 * 100)
score2.append(_score2 * 100)
print(f'k={_k} | score1: {score1[i]} | score2: {score2[i]}')
# Plot train score
plt.scatter(k, score1) #function
plt.plot(k, score1, '-', label='train') #data points
# Plot test score
plt.scatter(k, score2)
plt.plot(k, score2, '-', label='test')
# Plot configurations
plt.axis([max(k),min(k)+1, 0, 100])
plt.xlabel('number of nearest neighbours (k)', size = 13)
plt.ylabel('accuracy score', size = 13)
plt.title('Model Performance vs Complexity', size = 20)
plt.legend()
# Output
plt.show()
"""
k=1 | score1: 100.0 | score2: 40.0
k=2 | score1: 95.0 | score2: 60.0
k=3 | score1: 85.0 | score2: 100.0
k=4 | score1: 85.0 | score2: 100.0
k=5 | score1: 85.0 | score2: 100.0
k=6 | score1: 85.0 | score2: 100.0
k=7 | score1: 85.0 | score2: 100.0
k=8 | score1: 85.0 | score2: 100.0
k=9 | score1: 85.0 | score2: 80.0
k=10 | score1: 85.0 | score2: 60.0
k=11 | score1: 80.0 | score2: 60.0
k=12 | score1: 90.0 | score2: 60.0
k=13 | score1: 65.0 | score2: 60.0
k=14 | score1: 55.00000000000001 | score2: 60.0
k=15 | score1: 55.00000000000001 | score2: 60.0
k=16 | score1: 45.0 | score2: 60.0
k=17 | score1: 50.0 | score2: 60.0
k=18 | score1: 50.0 | score2: 60.0
k=19 | score1: 40.0 | score2: 40.0
k=20 | score1: 40.0 | score2: 40.0
"""
Decision Boundaries
Decision boundaries of KNN on a graph (optimal fit for k=5)
Last update: 515 days ago
