Lernen Implementing k-NN

KNeighborsClassifier

Implementing k-Nearest Neighbors is pretty straightforward. We only need to import and use the KNeighborsClassifier class.

Once you imported the class and created a class object like this:

# Importing the class
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=3)

You need to feed it the training data using the .fit() method:

knn.fit(X_scaled, y)

And that's it! You can predict new values now.

y_pred = knn.predict(X_new_scaled)

Scaling the data

However, remember that the data must be scaled. StandardScaler is commonly used for this purpose:

You must compute $\bar x$ and $s$ only on the training set using .fit() or .fit_transform(). Then use .transform() on the test set so both sets are scaled identically:

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

Using different scaling values for train and test harms predictions.

Example

We predict whether a person enjoys Star Wars VI using their ratings for Episodes IV and V (from The Movies Dataset). After training, we test two users: one rated IV/V as 5 and 5, the other as 4.5 and 4.


              123456789101112131415161718192021222324252627
            
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
import warnings

warnings.filterwarnings('ignore')

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/starwars_binary.csv')

# Dropping the target column and leaving only features as `X_train`
X_train = df.drop('StarWars6', axis=1)
# Storing target column as `y_train`, which contains 1 (liked SW 6) or 0 (didn't like SW 6)
y_train = df['StarWars6']

# Test set of two people
X_test = np.array([[5, 5], [4.5, 4]])

# Scaling the data
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Building a model and predict new instances
knn = KNeighborsClassifier(n_neighbors=13).fit(X_train, y_train)
y_pred = knn.predict(X_test)
print(y_pred)

War alles klar?

Danke für Ihr Feedback!

Abschnitt 1. Kapitel 4

Fragen Sie AI

Fragen Sie alles oder probieren Sie eine der vorgeschlagenen Fragen, um unser Gespräch zu beginnen

Swipe um das Menü anzuzeigen